What Does Really Matter in Technology Adoption and Use? A CCO Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it