Bibliographic record
Abstract
Why do we use technologies in technology studies? Couldn’t we teach technology in a classroom without the complex lab and workshop infrastructures that characterizes technology studies? We could argue that this is by tradition; this is the way it always was. We could argue that we are involved in training students for occupations that use the technologies we use. We could argue that technology is naturally practical and demands that we offer practical activities. Tradition, vocation, or imitation. Not one of these three will get us very far. We could argue that students learn best when they are active; enactive experiences are best. With this argument, we verge on theoretical issues that underpin technology studies. However, neither experiencebased learning nor enactivism account for technologies in any adequate way. We need to retheorize learning theory to make it work for technology studies. Learning theories deal with specific notions of feelings, knowledge, and skills by addressing the problem of how we learn. Whether we are aware or not, our teaching practices are necessarily shaped by any number of learning theories. We are conditioned or socialized to express particular learning theories through years of participation in schooling and informal education. Sayings such as “we teach who we are” or “we teach how we were taught” suggest the power of our socialization into education. We are all products of our formal schooling and informal education.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.052 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".