Enrolling Deceased Founders in Times of Change or Discontinuity: The evocative powers and perils of presentification
Bibliographic record
Abstract
Scholars have given increasing attention to the uses of the past in organizational life, with several studies considering how founder figures may be drawn on in sensegiving to help promote organizational change. Yet most research has been one-sided, neglecting the possibility that leaders’ sensegiving attempts might be contested. Based on three contrasting case studies, we identify four modalities through which deceased founders may be presentified (i.e., made present, despite their physical absence) in leaders’ sensegiving attempts and we examine how and why such efforts may be authenticated or contested by others. Our study contributes by showing how deceased founders may not be powerful figures in themselves, but are made powerful by being presentified in evocative ways that reach beyond citing words to calling up emotions, memories, and vivid imagery. The study highlights how audiences develop complementary strategies to either oppose leaders (through counter-presentifications) or to support them (through amplifying presentifications), adding friction or fluidity to the communicative process. Finally, the study illuminates contextual facilitators that explain how actors’ relative positioning with respect to founders and audiences allows certain individuals to tap into more privileged memory sources to presentify founders in more evocative ways.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".