Bibliographic record
Abstract
I n this chapter, the author approaches active offer from the angle of organizational culture.He presents the results of a survey of health professionals working in Anglophone and Francophone hospital facilities in New Brunswick.The organizational culture of these institutions is discussed in light of research in the sociology of organizations, not as a fate but as a construct, as are the rules of the organizational game (hierarchical relations, job description, collective agreements, and so on).The research reveals the predominance of an organizational culture centred on bilingualism, which leads to a persistent confusion between the notions of active offer and bilingualism.From this point of view, although bilingualism is essential to a high-quality active offer in both official languages, it can also be counter-productive when it comes to introducing a culture that is favourable to active offer.The findings show the emphasis placed on bilingualism is often perceived by unilingual people as a threat to the balance of powers within the system, frequently leading to resistance toward any measure favourable to active offer.Hence, the author suggests the value of a culture of active offer should be articulated in terms of the objectives of safety and high-quality care in the official languages.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".