by Heidi Norman
The second thing that troubles me is that international rankings are meant to identify the best workplaces, yet none of the rankings evaluate important indicators like job satisfaction, work–life balance or equal opportunity. Taken to the extreme, the research quality indicators might drive behaviour that leads academics to work ridiculous hours, taking no time off during the year, not celebrating their successes, and expecting the same work ethic from their team. In this scenario, leaders of the largest research teams would thrive, because they would produce more results than smaller groups, so that we might see the rise of academic Ponzi schemes.
With funding cuts to research and a growing number of large teams led by senior researchers, we might also see grant funding success fall to record lows and junior researchers miss out on funding. Those who work best in collaborative, cooperative settings will become disenfranchised and demoralised in the hypercompetitive environment that develops. Researchers might consider cutting corners, and the academic pipeline will likely leak first with those who have significant domestic and caring responsibilities. Perhaps we might observe an increase in mental health issues among academics.
Does this set of circumstances seem familiar to anyone else?
While there are good reasons to evaluate research quality and impact, it is inevitable that bad things will happen when no checks are placed on how the loftiest research heights are attained. If the goal of international rankings is truly to identify the best places to study and work, then new metrics are needed to identify institutions that combine achieving research and teaching greatness with offering the ‘best’ diversity in their professoriate, boasting the ‘top’ work–life balance, and supporting ‘leaders’ who train research students for positions outside academia.
With these thoughts in mind, I’ve dreamed up a few new metrics to use alongside the more traditional ones. Maybe this combination might lead to rankings that identify the most successful, most highly productive higher education training grounds and workplaces that are also best at supporting career aspirations and mental, emotional and physical wellbeing.
1. The no-asshole rule
At the Science in Australia Gender Equity Forum in Canberra in late 2014, I heard about the no-asshole rule. Assholes: we’ve all met them. We’ve all had to work with them. According to Bob Sutton who wrote the book on the no-asshole rule, assholes are defined by two characteristics:
• after encountering the person, people feel oppressed, humiliated or otherwise worse about themselves
• the person targets less powerful people.
If these characteristics apply, there is your asshole (figuratively speaking). Bob even outlined an asshole scoring system (ranging from levels 0–3) and management metric.
It is my view that in order to be eligible to participate in the new international rankings, universities must teach the no-asshole rule to all first-year students, employ no level 3 assholes and tolerate no level 3 asshole behaviour by staff or students (for one of too many recent examples, see the Dalhousie ‘gentleman’ student dentists). One asshole incident without consequences means the whole institution gets the big red dislike button.
2. H-index
No, not that one. This is the Happiness index. Bhutan has one; it’s called ‘gross national happiness’. I propose that staff and students at universities should be surveyed each year to measure their happiness in the workplace. Someone else has already done the hard part by working out the questions for Bhutan, though no doubt we’ll have to add a few new ones for academic happiness (e.g. fairness in allocation – and appropriate recognition – of teaching and service roles, and availability of high quality coffee – or tea and biccies in my case).
An essential indicator of happiness will be the annual leave ratio (ALR). This is a simple calculation:
ALR = ALT/ALA
where ALT is the combined number of days of annual leave taken by all staff (unprompted by HR) for the previous year, ALA is the combined number of days of annual leave available to all staff each year (with a minimum of 20 days per staff member). Universities where all available annual leave is taken by staff would rate highest with an ALR of 1.
3. F-index
The F-index is about fairness. Despite published pay scales, men are paid more than women in the upper echelons of academia for doing the same job. Because, ‘loadings’. In my perfect world, to be eligible to participate in international rankings, universities would make public the average pay for men and women in leadership (professoriate and above) by posting the data on the front page of their website at the start of each year. The F-index is calculated as follows:
F = W/M
where W is the average salary for women in leadership positions and M is the average salary for men in leadership positions. Universities with the highest ratio (and thus the smallest gender pay gap) would rank highest on international rankings. It would be a fun experiment to see how long it would take for this indicator of university ranking to reverse the gender pay gap in academia. Looks like the University of Sydney will skyrocket on this measure: the VC indicated at the ‘Women at Sydney’ event in late 2014 that remedying the gender pay disparity is a strategic objective for the university in 2015.
4. D-index
D is for diversity. Diversity is good. Differences in perspectives and methods of approaching problems lead to better outcomes. In Australia, our universities are populated by people of diverse culture, gender, age, socioeconomic status. Yet our leaders are mostly male and mostly white. The D-index measures how well the leadership teams at universities (professoriate and above) reflect the diversity of the broader university (all staff and students). For example, let’s take gender. Plenty of studies show that more women in the workplace, especially more women in leadership positions, is not only the right thing to do, it’s the smart thing to do because it’s good for business. Yet gender equity in leadership positions remains at dismally low levels (less than 20 per cent) across the board while male CEOs dig their heels in at quotas. To counter the entrenched system, the D-index for gender (Dg) will be evaluated:
Dg = GB(lead)/GB(all)
where GB(lead) is the gender balance or proportion of women in leadership positions (usually <0.2) and GB(all) is the proportion of women across all staff and students (usually >0.5). Most universities would have Dg values of 0.4 or below. Those who score the maximum D values of 1 would zip up to the top of university rankings. If the D-index was implemented, would we see an exponential rise in women, people of colour, Asian and Indigenous people in leadership positions? I’d sure like to find out.
5. K-index
K is for kids. One issue that crops up again and again is that primary caring responsibilities often fall to women, with a consequent reduction in their academic competitiveness (unless their track record is considered, fairly, relative to opportunity). So problematic is this issue that some women choose to forgo having children to remain competitive in their career. Why do we make it so difficult for the smartest women to reproduce? Wouldn’t it be good for the world, and for universities, if we made it easier? At the same time, male academics want to spend more time caring for their kids, but face stigma and lack of support from colleagues and bosses for taking time off for parental duties. Why do we make it so difficult for the brightest men to participate in the most important work of all? Wouldn’t it be good for the world, and for universities, if we made it easier? Germany dealt with this specific problem by awarding an extra two months to the standard 12 months paid parental leave when both parents took time off to care for their children. The K-index celebrates the birth of children to academics:
K = (A+3B+C+D)/E
where A is the number of days of parental leave taken by women over the past year, B is the number of days of parental leave taken by men over the past year, C is the number of childcare places on campus, D is the number of parenting rooms on campus and E is the total number of staff and students on campus. On this metric, those universities th
at best support and encourage families will rocket to the top of international rankings, and most likely will have to turn away large numbers of outstanding students and academics.
Endnote
You may say I’m a dreamer… well, you know the rest. I’m also something of a realist. No doubt if these indices are implemented, game-playing would follow with unintended consequences. Nevertheless, it’s been interesting to think about university metrics that might drive new, perhaps more socially just, workplace behaviours. Maybe next I’ll dream up some indicators for ranking individual academics …
The women who fell through the cracks of the Universe
Revisiting Milgram’s shocking obedience experiments
The women who fell through the cracks of the Universe
Lauren Fuge
In the early 1880s, astronomer Edward Charles Pickering had a problem: he had data coming out of his ears. Ever since being instated as director of the Harvard College Observatory in 1877, he’d been lobbying for improved astrophotography facilities. Finally, his observatory had the capabilities to image the stars for analysis at a later date, instead of the tedium of real-time nakedeye observations … and suddenly photos of star fields were being taken at a rate too rapid for Pickering’s staff to keep up with.
What’s worse, his assistant was useless. So Pickering did what any sensible astronomer would do – he fired his assistant and hired his maid, Williamina Fleming, as a replacement.
Fleming had a background in school teaching, not in astronomy, but she proved so adept at computing and cataloguing that Pickering soon hired a whole host of women as assistants and put Fleming in charge of them. In total, over 80 women worked for him during his tenure at the Harvard College Observatory. Pickering’s goal was to photograph and catalogue the entire sky, and these women – some of whom were educated astronomers – threw themselves into the work.
The women came to be known as Pickering’s Harem, or, as I prefer, the Harvard Computers, because they were essentially employed as human computers. They reduced the photographs of star fields to render them as clear as possible, performed complex calculations to determine the positions of the stars in the sky, analysed the light from each star to determine what elements they were composed of, and classified them according to catalogues.
It was incredibly important work in the history of astronomy. These women took the first steps towards mapping the universe, providing the foundations for larger astronomical theory to be developed over the next century. They discovered and deciphered new suns, and they contributed enormously to the first Henry Draper Catalogue, a catalogue of more than 10 000 stars classified according to spectrum, published by Pickering in 1890.
Although the implications of their data were huge, the work of the Harvard Computers was dry, tedious, and meticulous. They put in six-day weeks and worked for between 25 and 50 cents per hour, the wage of a factory worker. Some of these women were trained astronomers, and yet their tasks working for Pickering were essentially secretarial. At the time, the cold, damp conditions of an observatory dome were deemed unsuitable for women, but by only allowing his female assistants to work with photographs, Pickering could place them in more ‘appropriate’ office settings. He allowed some to make direct observations, but these were exceptions; mostly, his assistants were barred from producing real, theoretical work. There was little chance to progress to more important and demanding positions – after 20 years working at the observatory, Fleming was finally given the title of Curator of Astronomical Photos, i.e. ‘official’ supervisor.
The Harvard Computers are an example of the ‘harem effect’: a phenomenon where a male scientist in a position of power, such as Pickering as the director of a well-known observatory, hires a predominantly female staff for subordinate positions. The harem effect has pervaded the history of science for a couple of reasons. The lower rate of pay for women allowed for more employees to be hired with the same budget, which was especially important for Pickering, who needed to process large amounts of data efficiently. Women were also seen as less threatening than men in the same position – competent, but not perceived as competition for the male in power.
But the women who worked for Pickering deserve more than to be lumped together and dismissed as a sad sociological effect, as a product of the times. The Harvard Computers paved the way not only for modern astronomy but also for women in science, and they deserve to be remembered.
Some of the Harvard Computers produced their own notable research and obtained a level of respect among female scientists of the era. Take Antonia Maury, for example – a graduate from Vassar College, Pickering hired her to help reclassify the stars in his original Henry Draper Catalogue. Along with Pickering and Fleming, Maury worked on a system to classify stars based on their temperature. Her system, published in 1897, was largely ignored but later Annie Jump Cannon, a Wellesley College graduate, came to the Harvard Observatory and reworked the system again. Cannon’s redesign was developed into the Harvard Classification System, which has been adopted by the International Astronomical Union as the official worldwide system of star classification.
Annie Jump Cannon is perhaps the most famous of the Harvard Computers, though you’ve likely never heard of her. She specialised in analysing absorption spectra and was so prolific, she could classify over 50 000 stars per year.
‘They aren’t just streaks to me,’ Cannon said once. ‘Each new spectrum is the gateway to a wonderful new world. It is almost as if the distant stars had really acquired speech and were able to tell of their constitution and physical condition.’
She was awarded six honorary doctorate degrees for her influential work, including the first degree from Oxford ever presented to a woman. Cannon also won the Ellen Richards Prize, which was awarded by the Association to Aid Scientific Research by Women, but shortly after it was given to her in the 1930s, the award was disbanded. The organisers legitimately thought that the struggle of women in science had ended, and everything was now fine.
Cannon, thankfully, knew what was really up. She used her prize money to establish a new award for women in science, which was awarded to luminaries such as Payne-Gaposchkin and Maury, and is still helping female astronomers today.
Dozens of other Harvard Computers were notable for work they did beyond Pickering’s research. Margaret Harwood was later appointed the director of the Maria Mitchell Observatory, becoming the first female director of an independent observatory. Johanna Mackie discovered the first nova (a bright explosion caused by the reignition of a dormant star) in the constellation of Lyra, and was awarded the gold medal from the American Association of Variable Star Observers (AAVSO). Ida Woods was also honoured by AAVSO for discovering more than seven novae within the Milky Way galaxy. Henrietta Swan Leavitt discovered 2400 variable stars throughout her career. These stars regularly brighten and then grow dim again, and Leavitt developed the period-luminosity law still in use today, which gives a relationship between the brightness of a variable star and the length of time taken to go through one cycle and return to its original level of light. Cecilia Payne-Gaposchkin was a variable star expert who became the first person to obtain a research PhD at the Harvard Observatory, and later became the first female tenured professor at Harvard.
How many of their names do you know? If these women have been forgotten, then how many more thousands of women have helped discover fundamental aspects of our universe only to be swept under the rug, their names lost to future generations? How many landmark scientific projects involved women who were forgotten when it came to writing books, typing up Wikipedia pages and handing out Nobel prizes?
Craters on the Moon and Mars are named after Pickering, but his female assistants fell through the cracks into obscurity. Remember them. They mapped the stars – their names should still shine today.
Light
Job description
What shall we teach the children
Beating the odds
Trent Dalton
He whispered in a Scottish drawl: ‘You wanna see somethin’ cool?’
Professor John Fraser has been an intensive care specialist for two decades. He established the Critical Care Research Group at Brisbane’s Prince Charles Hospital in 2004. He’s seen cool things before: heart transplants; machines that can rebuild a blackened human lung before your eyes; bodies of children wrenched from the cold and still grip of beyond.
It was three years ago when he whispered the invitation, at a backyard barbecue at his house in Brisbane’s northern suburbs. We were eating sausages, talking about outdoor music systems controlled by one’s mobile phone. The things we humans can accomplish. Our wives went to school together. I’d known him for 13 years, long enough to know that when he asks if you would like to see something cool he’s not about to show you a Harley-Davidson motorcycle.
John led me into his house, up a set of stairs to the main bedroom. He said a genius had moved into the office next to his at Prince Charles, an obsessive young biomedical engineer who rarely ate, rarely slept, spent his days and nights clanging and banging and grinding away at strange metal objects. John was privy to the best-kept secret in Australian medical science. His name was Daniel Timms and inside his small solitary office in Chermside, Brisbane, he was building a miracle.
John unzipped a suitcase which lay in the middle of his bed. He had just returned from overseas. The surgeon dug his hand into a pile of travel clothes and pulled out a pair of dark green men’s underpants, moth-eaten and frayed. ‘Here you go,’ he said, placing the underpants in my hands. ‘Don’t worry, they’re clean.’
The underpants wrapped and protected something metal, lightweight and spherical, not much smaller than a tennis ball, weighing about 500 grams. Made of titanium, with clean design lines, it was so perfect and neat and unfamiliar that it felt alien, a product not of our Earth, not of our time. ‘The BiVACOR,’ John said.
