What's the Next Chapter for Strategy as Narrative?
Bibliographic record
Abstract
Some twenty years after Barry and Elmes (1997) seminal paper on strategy as narrative, the interest in the production and consumption of strategy narratives in the practice of strategy is undimmed. A better treatment of narrative has the potential to provide insight into the reciprocal relationship central to Strategy as Practice (SAP) scholarship (Whittington, 2006), between practitioners, praxis and practices. We can also better understand organizational processes and practices, notably around stability and change, through a fuller treatment of narrative, not least strategy as narrative. This adds up an unfulfilled promise of strategy as narrative, central to which is the relationship between strategy at organizational level and the broader societal or macro-institutional setting within which strategy is produced and the potential to build on and theoretical and empirical exploration of the process of narrative strategic practice. In this symposium we discuss and challenge the case for conceptualizing the relationship as one of 'intertextuality', providing the empirical and theoretical basis for developing strategy as an intertextual narrative practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".