Tracing Threads of In/Visibilities: The Knotty Mattering of Policymaking
Bibliographic record
Abstract
Abstract This chapter focuses on the actors who engage in policymaking to offer alternative understandings of informality and the context in which this occurs. Using ethnographic vignettes from Canada and Australia as illustrations, theoretical and methodological goals are pursued through adopting the anthropological concept of ‘traces’ to show how informality both mediates and transcends across non-fixed physical, temporal and conceptual boundaries. With an underlying premise that normative understandings of informality are shaped by the policymaking ‘black box’ metaphor and a lack of access to policymaking spaces and actors, this chapter argues against the association of informality with illegitimate and invisible policy processes. Instead, experience of the policy process gained through professional and ethnographic engagement, or an ‘insider’ perspective, shifts the researcher’s gaze beyond physical barriers or separations to show that ‘traces’ formed through in|formal encounters create opportunities for relationality through which policy is conceived, deliberated and, in part, created.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".