Bibliographic record
Abstract
It was not only in the hey-days of new historicism that the opening anecdote seemed desirable. In 2002 Christopher Hoadley and Roy D. Pea started an article with an anecdote as they began their exploration of support tools for what they saw or foresaw as the ways to create ‘a knowledge-building community’, the kind of abstracted phrase that is necessary for considering the sociology of such organisational matrices but which still makes my blood run cold. The anecdote, told at inordinate length, was chosen to illustrate the obvious truth that ‘[f]inding a professional connection with a colleague seems like a simple task but can devour hours of time’ (2002: 321). ‘David’, the name they give to the subject of their story, is in search of someone to work with on ‘interactive toys’. Keen to find a woman located in western Canada who had won an award for women in computer science and whose work he vaguely remembered having heard of, he searched on the web for her. Initially the search is unsuccessful: ‘[a]fter spending nearly half an hour, he decided to try a different strategy’ (321) and I hear the sound of horror lurking behind this appalling expenditure of time on a fruitless search. Now, using his social networks (by which the authors cannot yet mean Facebook), David finds that a colleague remembers the woman’s work being cited in a book by someone they name ‘Renee’ who was based in Los Angeles. Library catalogue and web searches for the book and its author waste a further ‘Ten to 20 minutes’, especially when he cannot locate Renee’s home page on the website of her university. Finally giving up on Renee, David searches the computer science departments of western Canadian universities and eventually locates his mysterious potential collaborator, though the ‘search odyssey lasted hours’ (2002: 322).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".