Matrixes for Data Collection in Information Research: Issues Related to Data Reduction and Display
Bibliographic record
Abstract
The goal of this lightning talk is to foster discussion about the effective analysis and reporting of data collected via interview-based matrixes. Data matrixes are a common qualitative data analysis tool. They are less common at the data collection stage. For this study of the information sharing practices of Brazilian undergraduate students, participants completed a written data matrix in conjunction with semi-structured interviews. The researchers will describe the project and engage audience members in discussing the merits of using matrixes for data collection, ideas for effective data reduction and display, and issues relating to reporting data and findings in translation.
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 | 0.502 | 0.741 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.038 | 0.031 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".