Bibliographic record
Abstract
For example, section 8(1) of Alberta's Human Rights, Citizenship and Multiculturalism Act is typical of most provincial human rights legislation in this regard. It reads: "No person shall use or circulate any form of application for employment or publish any advertisement in connection with employment or prospective employment or make any written or oral inquiry of an applicant ...that expresses ... any limitation, specification or preference ... or that requires an applicant to furnish any information concerning race, religious beliefs, colour, gender, physical disability, mental disability, age, ancestry, place of origin, marital status, source of income or family status of that person or of any other person." The theory in barring employer inquiries is that if the employer cannot ask the employee about these attributes, it will possess no knowledge of them and, accordingly, it cannot illegally discriminate by considering them. Even informal banter over dinner about an applicant's marital partner, children, and age is ill-advised. Inquiries about what an applicant did in previous jobs should reflect more on qualifications than on age. One should be careful about stereotypes about another's religious practices to avoid such assertions as, "We are a very collegial group. Often we eat out together, but you wouldn't be able to do that." Or "the fitness centre is excellent, but I don't suppose you would go there." Job interviews might be conducted with the applicant behind a screen so that the interviewer could not see what the applicant looks like. This would prevent a visual observation of such characteristics as the applicant's age, race, disability unrelated to performance, and gender. Even then, the applicant's voice would likely betray the applicant's gender and age. The screen would hinder visual observation of the applicant's grooming, dress, and demeanour, which are legitimate hiring decision factors.
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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.274 | 0.108 |
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".