Bibliographic record
Abstract
A neglected area of inquiry in management education history is how business schools construct their sense of history and contributions to the field. Unlike Schools of Law, Engineering, and Medicine, schools of business and faculties of management have largely lagged in developing a record of their past. Focusing on the Faculty of Management at Dalhousie University, authors investigate how a federated faculty develops their sense of belonging, defines a common sense of who they are and what they do, and regulates people (negotiating who is in and out) and resources (haves and have-nots). The study asks, “how does this federated faculty make sense of their shared history and identity?” Authors consider the temporal, geographic, structural, and conceptual borders and boundaries in which identity construction is transacted. Undertaking a socio-linguistic and an intertextual analysis of participant interviews and strategic documents to trace the trajectory of organizational discourse, authors examine how conflicts and tensions are enunciated, how social identities are cultivated, how borders and boundaries are constructed, and how behaviors and identities are regulated. The study illustrates how dominant discourses prescribe ways of thinking and behaving in shared academic space while also detailing the discursive effects on organizational actors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".