Bibliographic record
Abstract
Purpose This paper seeks to contribute to a better understanding of the impact on doctoral education attributable to performativity pressures in academia, by exploring the practices associated with the production of academic knowledge within the doctoral process. Design/methodology/approach An (auto)ethnographic inquiry was conducted over a period of ten months within the business school of a major Canadian university in order to examine socialisation practices and discourses from a given PhD program. Empirical observations from direct participation, local documents, and two interviews were analysed using a theoretical framework derived from Bourdieu's structural social constructivism and from Foucault's concepts of disciplinary techniques and technologies of the self. Findings The study shows how the doctoral program can be likened to a rite of passage, altering and shaping the cognitive structures and interpretive schemes of lay students – their subjective “selves”, their habitus. By means of a set of meticulous discourses and practices, the doctoral program changes novice researchers into disciplined and self‐disciplined academic performers, over time, to comply with the performativity rules of academia, while reflexivity can only be achieved through criticism and self‐criticism. Originality/value This paper focuses upon doctoral training vis‐à‐vis improve(ment) of economic and academic performance in a “knowledge society”. It mobilises and develops the notions of rite of passage, performativity, habitus, disciplinary techniques and technologies of the self to examine the conditions within which doctoral students somatise the ways and customs through their engagement in academia.
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 | 0.074 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".