From agency to structure: Analysis of an episode in a facilitation process
Bibliographic record
Abstract
In this article, we analyze an exercise in a facilitation process by showing that the structuring of this episode can be studied just by highlighting how different forms of agency (human and non-human) articulate with each other. The objective of this study is threefold: first, it aims at demonstrating that structuring effects can indeed be identified through a bottom-up approach without resorting to any form of duality or dualism, as it is common to think in the traditional literature in organizational studies (Conrad & Haynes, 2001); second, through this analysis, it illustrates the analytical power of such an approach by showing how it allows us to identify specific strategies used by the facilitators to do their work, especially in the way they select who or what is acting in a chain of agencies; third, it illustrates how the attribution of agency to artifacts allows human participants to progress throughout the facilitation process by enabling them to objectify what they are supposed to think and wish for, a process that Weick (1979) has identified as the bulk of organizing processes.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".