Theorizing Supply Chains with Qualitative Big Data and Topic Modeling
Bibliographic record
Abstract
The availability of Big Data has opened up opportunities to study supply chains. Whereas most scholars look to quantitative Big Data to build theoretical insights, in this paper we illustrate the value of qualitative Big Data. We begin by describing the nature and properties of qualitative Big Data. Then, we explain how one specific method, topic modeling, is particularly useful in theorizing supply chains. Topic modeling identifies co‐occurring words in qualitative Big Data, which can reveal new constructs that are difficult to see in such volume of data. Analyzing the relationships among constructs or their descriptive content can help to understand and explain how supply chains emerge, function, and adapt over time. As topic modeling has not yet been used to theorize supply chains, we illustrate the use of this method and its relevance for future research by unpacking two papers published in organizational theory journals.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".