Researching the Researchers: Gathering Data on Academics’ Use of Technology
Bibliographic record
Abstract
Bonded design, a participatory design methodology developed by information science researchers, is used as the framework for a university-wide initiative, the faculty IT liaison program, where faculty members and IT professionals work together as peers in design teams to examine and assess technologies. Bonded design met the program criteria: a limited and finite number of design sessions, opportunities to analyse data in situ to inform an iterative design process, and a framework to help two disparate groups (users and designers) to interact and collaborate with one another to generate innovative ideas for designing more userfriendly technologies.Bonded Design (BD), une méthodologie de conception participative développée par des chercheurs en sciences de l'information, est utilisée pour une initiative à l'échelle universitaire, le programme de liaison en technologies de l’information où les membres du corps professoral et les professionnels de l'informatique travaillent ensemble en tant que pairs dans des équipes de conception pour examiner et évaluer les technologies. BD répond très bien aux exigences du programme : un contexte où le temps est un facteur critique, les données collectées sont analysées in situ pour guider un processus itératif, et où deux groupes disparates de personnes ayant des domaines d'expertise différents doivent interagir pour construire un livrable qu’ils n'auraient pas pu développer seuls.
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 | 0.093 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".