Quantitative and Qualitative Content Analysis of Text and Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it