Bibliographic record
Abstract
This interview was carried out via Skype on 21 June 2013. Hockey was provided with the core questions in advance of the interview. Here she recalls how her interest in Humanities Computing was piqued by the articles that Andrew Morton published in the Observer in the 1960s about his work on the authorship of the Pauline Epistles. She went on to secure a position in the Atlas Computer Laboratory where she was an advisor on COCOA version 2 and wrote software for the electronic display of Arabic and other non-ASCII characters. The Atlas Computer Laboratory was funded by the Science Research Council and provided computing support for universities and researchers across the UK. While there she benefitted from access to the journal CHum and built connections with the emerging Humanities Computing community through events she attended starting with the ‘Symposium on Uses of the Computer in Literary Research’ organised by Roy Wisbey in Cambridge in 1970 (probably the earliest such meeting in the UK). Indeed, she emphasises the importance that such gatherings played in the formation of the discipline. As well as discussing her contribution to organisations like ALLC and TEI she recalls those who particularly influenced her such as, inter alia , Roberto Busa and Antonio Zampolli. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".