Bibliographic record
Abstract
The observation that we are surrounded by rapid technological change is a truism. One need not look very far to count recent products of scientific and technological innovation. Just as I look around my office, the newcomers include a desktop computer (and all that entails), MP3 player, laser printer, cell phone, microwave, Gortex jacket, polyester fleece gloves (it’s February), recyclable plastic lunch container, debit card, streaming radio, the list goes on . . . and this is hardly a technological paradise. These are what we take to be ordinary objects. So, to note that the world has changed is to state the obvious. What is not as obvious and what has never been more important to consider is how we deal with that change. New knowledge requires new perspectives. Often these new perspectives on scientific discovery and technological innovation inspire a kind of joyful wonder. On the other hand, the uncharted territory of the newly discovered also inspires a certain amount of anxiety, as we contemplate difference and we contemplate change. Wonder and anxiety as they relate to scientific “progress” are a central theme for this issue of CTR.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.043 |
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".