Bibliographic record
Abstract
In 2017, Canada issued the world’s first health card for a baby that does not state the child’s sex. Requested by non-binary, transgender parent, Kori Doty, who identifies as neither male nor female, the British Columbian ID carries a ‘U’ in the box where a child’s sex is ‘normally’ specified. But when it comes to gender and our interpretation of it today, what exactly is ‘normal’? Yes, gender is certainly the topic of the decade. The sheer mention of the word that originated from the Old French ‘gendre’ or ‘type’ and the Latin ‘genus’ meaning ‘birth, family or nation’ is likely to ignite multifarious opinions, wherever and whenever it is raised. We are living in an era of economic, social and environmental flux, witnessing an increased societal consciousness of the need for justice, fairness and the rejection of previously accepted norms. Sharing is at the heart of these shifts and is not just about tangible asset sharing, but offers a redefinition of ‘other’, a sharing of power and an understanding that what has previously dominated won’t do so for much longer. 2018 marked 100 years since women in the UK first got the vote, yet we are still subjected to sexism, violence and are vastly underrepresented in the political realm worldwide. The unacceptable statistics speak for themselves – each minute, 28 girls are married before they are ready and up to 35% of women today have experienced sexual or physical violence. The gender pay gap knows no borders; whether you’re a farmer in Nigeria, or Jennifer Lawrence in Hollywood, it’s likely you’ll only be paid two-thirds as much as your male equivalent. But something is shifting. From #MeToo to the Time’s Up campaign,53 women have had enough. We’re witnessing the emergence of a Sharing system based on fairness, mutual respect and caring. Often cited as a strong attribute of femininity, though some would say that is a stereotype too, sharing is indissolubly linked with caring, a quality that is being applied to create new systems that empower all genders rather than maintain the patriarchal status quo. In the past five years, we’ve seen a proliferation of initiatives enabling women to share everything from cars to cash.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.014 |
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".