Bibliographic record
Abstract
These lists of personal characteristics are called prohibited grounds of discrimination in employment. In the United States, the same lists are called protected classes. If the personal attribute is on the list, the employer must be blind to it. One cannot consider that attribute in any decision unless the attribute can be clearly demonstrated to relate objectively to the job. For example, if fire fighting requires extraordinary physical strength to do the job, fire departments might justify fitness testing that disproportionately screens out disabled, elderly, or female prospects. Likewise, safety concerns in a construction site might override religious beliefs if the worker will not wear a hard hat. Equality through non-discrimination is a social construct, given effect through law. The model which Canada has chosen to use is the prohibited grounds of discrimination framework. It is thought to provide more specificity and efficacy than simply to legislate that everyone is equal before and under the law. However, one might ask whether the list of prohibited grounds of discrimination is itself discriminatory. If we compare the current lists of prohibited grounds of discrimination against these three criteria, we will find some which do not warrant being there. For example, ancestry, place of origin, ethnic origin, and race seem unnecessarily duplicative. In contemporary multicultural Canada, is one's ancestry or place of origin really visible and a factor in employment decisions compared to race or ethnic origin? Is sexual orientation visible? What about religion? In Ontario, why are citizenship and record of offences not relevant in every employment? One might argue that they should be permitted grounds of discrimination.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".