Who Am I and What Should I Do? Identities and Moral Orders in Canadian and Finnish Business Research
Bibliographic record
Abstract
What kind of research should be done in business schools? This has been debated since business school faculty started to conduct research, but a lengthy debate has centered on the 'rigor versus relevance' of business school research, as well as on the appropriateness of different research modes. We pursued this question at the micro level, investigating what kind of narratives business school researchers themselves follow and what kind of identity positions are available to them to adopt. Comparing the narratives and identity positions of researchers in two different contexts-Canada and Finland-through a qualitative analysis of interview transcripts, we found more contextual and local variation than what the rigor versus relevance debate and the studies on the business school research modes suggest, but also dominant forms of research appreciated more than others within local moral orders. We identify and analyse four different narratives and identity positions of Canadian and five of Finnish business research. These construct different moral orders in each context, shaping the kind of knowledge produced and how business school researchers find meaning in their work. Implications for both the researchers and the school administrators and funding agencies are discussed.
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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.074 | 0.068 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".