Bibliographic record
Abstract
In response to Maureen Flint’s (2020) performance and essay, Fingerprints and Pulp, on the ethics of truncating and flattening research participants in qualitative research, I extend this ethical concern to the voices of scholars flattened in qualitative research and writing processes. Scholars cite for many reasons, but what is there that holds us to account for our treatments of academics that come before; how can we avoid flattening and abusing those we cite? Through endeavouring to recognise and protect ghosts and nomadic identities of those other than the author in the research and writing process, I propose a new way of re-animating and re-embodying the haunting, nomadic voices in cited texts, in order to minimise further, future truncations and limitations of the other in academic writing. Attending to the ghosts allows for more ethical and just behaviour towards those cited. Seeing the multitude of ghosts haunting scholarly work obliges more ethical behaviour toward those voices flattened in writing.
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 | 0.059 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.063 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".