Bibliographic record
Abstract
Purpose In the spirit of the growing Time is Up movement in North America, this paper aims to focus on the human dimension of academic learning environments and delves into the reasons for the continuous oppression, discrimination and bullying (ODB) of faculty members with disabilities in academia, showing the particularly detrimental effect of ODB in the small professionally oriented field of information science. Design/methodology/approach The conceptualizing of continuous ODB of people with disabilities in academia is done by carefully scrutinizing the state of affairs; presenting a nuanced survey of utilized terminology; providing a new and inclusive definition of everyday oppression; introducing a new model of an oppressive workplace environment experienced by people with disabilities; showing the centrality of information behaviours and phenomena in ODB; highlighting the high relevance of this discussion to learning science; and outlining potential detrimental effects of ODB on the psychological climate in and the process of professional higher education. Findings The model of an oppressive workplace environment experienced by people with disabilities is presented. Originality/value Unlike previous models of ODB at the workplace, the current model puts information phenomena as decisive factors in continuous ODB against people with disabilities; particular attention is paid to information avoidance behaviours; distorted or delayed information messages transmitted by managers to employees; gossip as an informal information-based tactic of ODB; the insufficient protection of privacy and confidentiality of information about disabilities and personal health; and vague information messages that diminish the usefulness of university policies on disabilities.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.226 | 0.103 |
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".