From bricolage to thickness: making the most of the messiness of research narratives
Bibliographic record
Abstract
Purpose The research process is commonly viewed as a succession of linear, structured and planned practices that exclude informal and unplanned practices, engaging with the unexpected or the uncertain. The authors’ aim is to explore this aspect of researching in connection with the narratives of researchers as they oscillate between past and present, theory and empiricism. Design/methodology/approach The authors first draw on the concept of “bricolage” to validate informal research practices as researchers seek to lend “thickness” to their research. To deal with the apparent “messiness” of research narratives, they apply the concepts of kairotic time and action nets. Kairotic times are key moments in research narratives when actions, under tension, interconnect to form action nets, which, in turn, generate meaning or knowledge. Findings The authors analyse two research episodes. The first recounts how personal experiences and contingencies influence a researcher's choice of research objects and his associated theoretical reflections. The second highlights how some concrete difficulties in choosing a field and gaining access trigger a set of actions that force a researcher to review his initial choices and to reposition himself methodologically. Discussing the concept of kairotic time, the authors show the importance of context and timing and demonstrate how stories build around a gravitational point. From there, they discuss how the concept of action nets, breaking linearity, helps to envision research practice not as a sequence, but as networks of actions that produce scientific outcomes. Originality/value This paper provides an operational method of using kairotic time and action nets to account for, and acknowledge, the messiness in research narratives.
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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 | 0.057 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.032 | 0.053 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".