Bibliographic record
Abstract
Purpose In the context of increasing interdisciplinarity in academia and professional practice, the purpose of this paper is to focus on the contribution of information science (IS) to education and practice in social work (SW), specifically in the area of disabilities at the workplace. As a case in point, a work environment of academia and faculty members with disabilities and their managers are chosen. The paper also stands to improve interdisciplinary understanding between IS and SW. Design/methodology/approach Combining SW and IS perspectives and building off selective exposure, cognitive dissonance and uncertainty management theories, the paper looks at one of the root-causes of continuous workplace discrimination against and bullying of people with disabilities – information avoidance (IA). Findings The paper conceptualises discrimination and bullying as an inherently information problem, for which an SW solution could be proposed. Two types of information are noted to be avoided: information about disabilities and information about the effect of discrimination and bullying on employees with disabilities. The paper distinguishes between defensive and deliberate IA, each of which poses different challenges for social workers who are likely to intervene in the cases of bullying and discrimination in their capacity as workplace counsellors and advisors. Originality/value It is the first known paper that explores the intellectual and practice-based synergy between SW and IS in application to change-related interventions and preventative plans that counteract discrimination against people with disabilities at the workplace. It proposes creative solutions for intervention, including bibliotherapy. It also opens up a broader conversation on how critical the knowledge of IS is for social workers.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".