Quantitative and Qualitative Content Analysis of Text and Images
Bibliographic record
Abstract
Abstract Content analysis is a research technique that allows recreational-fisheries researchers to draw conclusions about the world from images and textual data. In this chapter, we present detailed guidance for content analysis, ranging from formulating the research question to visualizing the results, and including quantitative and qualitative approaches to coding, analysis, and interpretation. Drawing from a rich pool of over 20 studies from recreational fishing research, we find that the method has many applications in human dimensions and beyond. It is used to describe and explain opinions, arguments, and the behaviour of recreational fishers. Because content analysis can be applied to a variety of different data sources, the approach is particularly suitable for interdisciplinary research that bridges disciplinary boundaries and enables a holistic view of complex systems and behaviours. Popular applications in recreational fisheries include comparative and longitudinal studies, using diverse data sources such as transcripts of interviews, policy documents, and media excerpts. While highlighting the method’s strengths, we also illuminate instances where it may not be the optimal choice, elucidate challenges when coding certain material, such as images, and give an overview of helpful programming languages and software applications. Future developments promise exciting opportunities, propelled by technological advancements and evolving research paradigms.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".