Bibliographic record
Abstract
It’s hard to say why and even how commemorative issues of established publications such as Interacting with Computers happen. Certainly, the larger-than-life stature of especially early, founding agents in a discipline inspires what has been phrased recently (in a far different context) as “shock and awe”. It’s so very easy in hindsight to understand the understandings – the mental models – that progenitors contribute to this current world. All of the pieces seem to fit, intuitively, as if there was no chance for them to have turned out differently. The fact is that regardless of the field, the early shapers of disciplines are by definition geniuses who, in another very different context, went where no one had gone before. In important ways, this was Brian Shackel – or is, given his continuing influence on Human–Computer Interaction (HCI). It was not at all obvious in his early days that things would turn out as they have.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.043 | 0.051 |
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".