Intentionality and Attentionality Dynamics in an Institutional Change Process
Bibliographic record
Abstract
Abstract In this article, we explore how actors' intentionality emerges, develops, and co‐evolves with institutional change. Although intentions are essential in shaping institutional change agents' motivations and actions, our understanding of their dynamics is limited and biased by the assumption that intentions are usually identifiable prior to institutional change. Building on the works of philosophers, in particular Husserl's life exploration of consciousness and intentions, we argue that a more effective way to conceptualize intentionality in institutional change is to consider attentionality, that is, how actors direct their mental focus toward specific elements of their social reality. Empirically, we draw upon a phenomenon‐driven study of the Algerian agricultural transformation from 2000 to 2019, with a focus on the contributions of Benamor, a leading agri‐food business. We theorize a process model that differentiates between passive and active intentions. Passive intentions are virtual mental possibilities upon which active intentions are built to instigate change. Our findings highlight attentional conversion as a crucial mechanism that drives the transition from passive to active intentions. These findings have important implications for our understanding of institutional change theories.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".