Overcoming the stickiness of concepts: the interplay between the barriers to theory building and creativity
Bibliographic record
Abstract
Theory building is not only a set of procedures related to the statement of concepts and their relations, but also requires transferring knowledge. Concepts used to construct and develop theoretical contributions must move from the minds of authors to the minds of their audience. This social-organizational process is inherently creative but also fraught with barriers. In this article, we propose a novel model of theory building involving a knowledge transfer process, emphasizing the dialectical interplay between the barriers to theory building and creativity. Drawing inspiration from Gabriel Szulanski’s work, we submit that the process is particularly “sticky”: Unless the theory is adapted to meet various criteria as it progresses through the phases of construction, it is unlikely to reach the end of the process successfully. Still, contrary to the conventional perspective on knowledge transfer, which views "stickiness” as entirely detrimental and assumes that removing barriers always facilitates theory building, we propose an alternative approach. In our model, barriers are seen as both impediments and stimuli for theory building. This dual nature of barriers requires strategic consideration, particularly when aiming to eliminate or mitigate the most harmful forms of stickiness from theory building without disregarding their potential to foster creativity. Our integrated, knowledge transfer-based approach uncovers new strategic ground for addressing the barriers to theory building, making the process socially fluent and more rational.
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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 | 0.098 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".