Bibliographic record
Abstract
Reacting to the symbolic features and historical artefacts that invite institutional self-reflection at the Royal Military College (RMC), I created a performance project leading to two storytelling events. Everyday campus life at RMC already offers opportunities for cultivating a meta-perspective – a higher-order awareness – of the institution, and the storytelling events called attention to such opportunities. I argue that, likewise, art-based projects in the humanities call attention to the creativity – the making – involved in the humanities more broadly. The first storytelling event, Tailor Made (2017), comprised stories focused on the uniform as a model and the body wearing it as an actual bearing out that model. Social and cultural life is made of the difference between models and actuals, and each story engaged the ways in which rules, systems, and practices meet with individuals in hurtful, inconvenient, funny, or messy ways. The second event, Skylarking (2018), included stories of the institutionally condoned pranks called “skylarks” and coincidentally occurred against the backdrop of a campus-wide punishment that elicited a skylark response. This event and its context showed that marking disruption with more disruption (marking failure with punishment and marking punishment with prank) is a recursion that invites higher-order thinking about existing orders.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".