Narcissus Revisited: The Values of Management Academics and their RoleinBusiness School Strategies in the UK and Canada<sup>*</sup>
Bibliographic record
Abstract
The myth of Narcissus provides an appropriate metaphor for the continuing debate over the relationship between academics, business and other stakeholders; most recently expressed in terms of Mode 1 and Mode 2 knowledge and academic entrepreneurialism. Both myth and debate are based partly on conflicts over identity. However, surprisingly little empirical work has been conducted on the identity of management academics. A step towards this is made here by exploring the role of embedded and enduring values as a primary element of academic identity in business schools. Contextualized in the Mode/entrepreneuralism debate, a layered metaphor of academic organization is adopted, in which values are located among deep‐set constructs and a comparative and longitudinal perspective employed. A value‐ranking instrument is devised, applied and retested over five years in two business schools in Britain and Canada. This reveals values that are widely held by management academics and those that are more pervasive in each institution. Understanding such values helps provide insight into the strategic role of academic identity, grounded in ontological and epistemological frameworks.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".