Bibliographic record
Abstract
It is quite normal for research in innovation studies to include some discussion about the history of a regional-industrial context before engaging with primary data. But readers are typically asked to take the authors' expert knowledge of the historical context for granted. Instead, this chapter uses an ANTi-History approach to (re)assemble three histories of one ocean science and technology sector in Nova Scotia, Canada. It examines three incompatible newspaper and magazine accounts of this sectors' emergence – from 1960, 1980, and 2012. The earliest account of the sectoral history positions scientists, scientific instruments, science organizations, and geopolitics as key actors. But in the latest account, scientific instruments are not present; the main actors are private companies and science is lauded as the knowledge base that supports these companies. These three different histories are traces of efforts to define a sector/cluster/industry identity and to rhetorically impose that identity on various actors. They are 'rhetorical histories' that aim to 'assemble' a cluster as historical fact, thereby establishing a regional competitive advantage. Treating industrial history in this way demonstrates the need to take historical method (that is, historiography) seriously in research on innovation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 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".