Ghosts in the Hallways: Unseen Actors and Organizational Change
Bibliographic record
Abstract
When considering successful organizational change strategies, the prescriptions usually include some strong sense of leadership; a champion for the cause of change. Likewise there is often the suggestion of the requirement for a commitment to change on the part of others in the organization. Yet organizations and their associated actors are held in a social context which is both fluid and persistent at different times and locations. This study suggests that we may gain some useful insights about organizational change through following the breadcrumb trails that these actors leave in their stories about what they did and how change happened. Through employing actor network theory (ANT) and following the trails found in interviews regarding change at an eastern North American community college, this study explores the intersecting stories and persistent actors that contribute to the implementation of an organizational change strategy. This is an examination of the particular situation of a change leader who leaves the organization part-way through the story. In his account, it becomes discernable just how some actors become more or less persistent, indeed punctualized, allowing an examination of the manners in which such actors are able to enroll others in their cause. This tracing of how the messages and enactment of change (or lack of change) persist allows the uncovering of evidence concerning durable actors. This is especially poignant in situations involving invisible or absent actors such as this organization's retired Chief Executive Officer, and thus has the potential to reveal some important attributes of persistent actors in organizational change situations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".