Hard or Soft Searching? Electronic Database Versus Hand Searching in Media Research
Bibliographic record
Abstract
It is important for qualitative media researchers to consider the impact of their research objectives on the sample frame imposed and subsequent data-collection methods. To illustrate this, we present some of the issues we encountered in determining a method of gathering physical activity articles in daily newspapers. We consider the implications of search choices for our sample, highlight the impact of using hardcopy hand-searches and electronic indexes and emphasise the importance of conducting a study to determine the reliability of hand-searching versus electronic index search methods. We suggest that researchers should be aware of the benefits and drawbacks of search methods including what kinds of information these methods yield and the possible effects on the research project. We conclude by highlighting the importance of these discussions to the reliability of content analysis. URN: urn:nbn:de:0114-fqs0703204
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 | 0.240 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".