A Systematic Methodology for Preserving the Whole in Large-Scale Qualitative-Temporal Research
Bibliographic record
Abstract
Large-scale qualitative-temporal research faces significant data management and analysis challenges due to the size and the textual and temporal nature of the datasets. We propose a systematic methodology that employs visual exploration to produce a purposive sample of a much larger collection of data, followed by a combination of thematic analysis and visualization. This method allows for the preservation of the whole, producing thematic timelines that can be used to elucidate a narrative of incidents or issues as they unfold. We present a step-by-step guide for this methodology and a comprehensive example from the domain of social media analysis to illustrate how it can be used to reveal interesting temporal patterns among tweets relevant to a noteworthy incident. The approach is useful in sport management, particularly for research related to fan behavior, critical incident management, and media framing.
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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 | 0.363 | 0.353 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".