Creating Interstitial Spaces to Encourage the Genesis of New Practices
Bibliographic record
Abstract
We develop a typology of interstitial spaces showing that these arise at different points of between-ness, and that in different interstitial spaces new practices are generated from between different elements. In contrast to established models that suggest interstitial spaces occur rather naturally at the interface of fields, we show empirically that interstitial spaces are created at field overlap, at the interface of fields, and around new world views. Our empirical setting sits at the interface of an addiction treatment field. Our in-depth study of this setting allowed us to uncover the different interstitial space created by actors to encourage the genesis of new practice. We contribute to the literature by identifying where different interstitial spaces are created, the point of origin of new practices in different interstitial spaces, and the different micro-interactions that occur in these spaces. Our findings illuminate the agentic creation of interstitial spaces formed to encourage new practice.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".