What Does Really Matter in Technology Adoption and Use? A CCO Approach
Bibliographic record
Abstract
Building on Orlikowski’s reflections on sociomateriality, this article argues that we have to stop separating the material and the social to be able to precisely account for what matters in technology adoption and use, and that one way to do this is to take people’s matters of concern seriously. This means two things: taking into account all the matters of concern that come to express themselves in conversations (whether related to tools, rules, documents, principles, etc.) and not just the people who voice them, and showing how some of these concerns start mattering more than others by connecting with other matters of concern. To demonstrate the theoretical and empirical value of this approach, we analyze two interactional episodes taken from our longitudinal study of the introduction of a wiki at the French National Agency for Radioactive Waste Management.
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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".