Bibliographic record
Abstract
This Major Research Project (MRP) was a journey more than a destination. Although there was a distinct outcome, much was revealed through the methodological processes that made the particulars of it possible. \n \nThe journey had a two-pronged approach. Firstly, it dove into the essence of women’s experiences of microsexism in the workplace. It studied how subtle bias often unconsciously serves to sideline women’s self esteem, careers, and advancement. Then into the heart of Canada’s legislative, institutional, and organizational inability to close the gender wage gap, and to achieve balanced representation at senior levels and on boards. All of which ladders up to negatively affect a business’s success and the macro economy. A revelation along the way was that male allies are vital to eliminating microsexism in the workplace, that without their belief in its existence and participation to eliminate it, gender equality in the workplace will remain stalled. Most men are willing but may not know how. \n \nSecondly, it was a journey through the Ethnographic Experiential Futures (EXF) method of using ethnographic research to design images of the future as an experience. It was a process of discovering firsthand how ethnographic research data can be analyzed and synthesized to design an experience that resonates with its audience. The outcome is an immersive installation called Kaleidoscope Anima. It uses light, sound, mirrors and colour to shower participants with emotional triggers as they move from the present status quo workplace culture toward a preferred future of gender equality and economic stability. It is followed up by a four-point strategic plan, which outlines easy to implement tactics for individuals to use at work. The suggestion is that after experiencing the powerful emotions from the installation, participants may be left with an intent but no means. The four steps—think, speak, act and invest—guide, rather than scold, male allies to recognize the potential of a gender equal workplace to be a win-win for their own careers, business and personal lives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".