Bibliographic record
Abstract
Ensuring equal liberties requires neutral, i.e. impartial, settings where nobody would be deprived of freedom because of their personal characteristics. Religion and disability appear as characteristics which may clash with the existing social and physical environments. Therefore, the necessity of adjusting the existing environment, i.e., reasonable accommodation, is mostly discussed in reference to religion and disability. I aim to discuss reasonable accommodation from a different perspective and ask whether reasonable accommodation should be extended to age issues. I propose that age can lead to differences in conscience or culture like religion. Age can also be a source of dis/ability so it can be compared to accustomed disabilities. Eventually, age may also clash with the existing social and physical environments. I further propose that age is not only similar to but also different from religion and disability when it comes to reasonable accommodation. Therefore, I defend, reasonable accommodation should be extended to age in a special way. The next question then is how age could be accommodated under the European Union (EU) law, especially when we consider that reasonable accommodation law does not have a wide scope in the EU, unlike in Canada.
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 | 0.009 | 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 | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".