Bibliographic record
Abstract
Within the last decade, bricolage, as an approach to qualitative inquiry, has gained popularity in academic circles. However, while conceptual and concrete precedents exist, the approach has remained relatively misunderstood, and unpopular, in broader research communities. This may be because the complexity of the approach has stymied widespread discussions and commentary. This article means to address this concern by providing a thick, yet accessible, introduction to bricolage as an approach to qualitative inquiry. While researchers and scholars have conceptualized bricolage, few have attempted to provide an overview of how the concept emerged in relation to qualitative research. Further, while the literature on bricolage offers invaluable conceptual insights, lacking is a survey that provides clear examples of how bricolage has been implemented in research contexts. Therefore, while greatest attention in this article is devoted to contextualizing bricolage and introducing influential theorists, it also provides key examples of research that adopts the bricolage approach. In drawing on a plurality of sources, the article provides a thick discussion of the complex bricolage project; one that can be beneficial to both novice and seasoned researchers who pursue alternative methodological approaches.
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 | 0.089 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.017 | 0.107 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".