Taking a stand: The embodied, enacted and emplaced work of relational critique
Bibliographic record
Abstract
We reflect here on our experience as critical scholars in an academic organization when confronted with an expectation that we remain value-neutral about a political act which we, and many others, found reprehensible. Our experience relates to the Academy of Management response to the travel ban implemented by President Trump in January 2017 which denied US entry to citizens from seven Muslim majority countries. By exploring how the concept of ‘taking a stand’ was used by the Academy of Management leadership to try to silence politics, and the response that this generated within the critical management studies community, we draw attention to the impossibility of separating management scholarship from questions of ethics and politics. We highlight the gendered nature of struggles to be critical in uncritical spaces and draw attention to the importance of embodied, enacted and emplaced work as the basis for developing relational practices of critique.
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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.128 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".