Authors of misfortune: interpretation and expertise in a model disaster
Bibliographic record
Abstract
Abstract Since 2001, beetles have killed two‐thirds of the pine trees in British Columbia, Canada, decimating the predominant commercial tree species in one of the world's largest timber economies. Attempts to construct and circulate computer models of the infestation and its aftermaths, however, have obscured destabilizing changes across state institutions for environmental research. Juxtaposing literary conceptualizations of distributed authorship with ethnographic critiques of technoscientific bureaucracy, this article examines how the proliferation of computer models in contemporary resource planning institutions has altered the ways experts participate in and sanction interpretive communities. The dynamic conceptualizations of authorship produced through these exchanges challenge existing portraits of anticipatory governance, an emergent mode of administration that often relies on models for procedural implementation and narrative framing even as it circumscribes modellers’ voices to specific moments of interpretation and critique. While modellers make claims on distant futures to provoke discussion among diverse actors, later interpreters may highlight a model's apparent precision or its radical uncertainties to defer criticisms of problematic interventions and government restructuring. Such modes of attribution have deepened many scientists’ sense of estrangement from the interpretive communities their models help to engender.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.052 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".