Fishing via Meaning Infrastructure: Entrepreneurial Search and Possibility Development in the Emergent AI & ML Field
Bibliographic record
Abstract
This monograph broadens the scope of cultural entrepreneurship by exploring the link between entrepreneurial search activities and the level of institutionalization of fields. Cultural entrepreneurship involves manipulating cultural repertoires to gather support from relevant audiences, but this relationship between institutionalization and entrepreneurial activity has been underexplored. Additionally, the assumption that entrepreneurs and their target audiences share cultural overlap is not always true in fields with a low level of institutionalization. These aspects have limited cultural entrepreneurship's ability to explain, for example, entrepreneurial search and possibility development in nascent and disintegrating fields. To address these limitations, this dissertation proposes the metaphor of fishing as a mechanism through which entrepreneurs explore possibilities and advance or demobilize institutionalization. The concept of meaning infrastructure is also introduced, representing a network of cultural repertoires that constitute the underlying meaning system of an institutional field. Four archetypes of meaning infrastructure are theorized: ethereal, condensed, plasmatic, and crystallized. Successful fishing occurs when entrepreneurs develop ties among cultural repertoires at the meaning infrastructure level. Empirically, this dissertation examines how cultural repertoires become available for startup organizations in an emerging field (i.e., the artificial intelligence and machine learning field in Canada between 2011 and 2020) and how fishing strategies are used effectively. Four major cultural repertoires are available for startups in an emerging field, enabling four fishing strategies: visionary, steward, communitarian, and pragmatic. The pragmatic strategy focusing on a crystallized infrastructure from adjacent mature fields is the only one that increases the chance of securing initial funding. This dissertation concludes with a discussion on the overall significance of the responses to the three research questions addressed, as well as directions for future research. Theoretically and empirically, this monograph contributes to advancing the relational turn in cultural entrepreneurship, a move that is fundamental to understanding entrepreneurial activity as part of a broader macro-cultural context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".