The role of conceptualization and operationalization in the use of secondary data
Bibliographic record
Abstract
With the recent advancements in data mining and availability of large data repositories, a vast amount of collected data are reused as secondary data sources. Although the use of secondary data provides many new opportunities for knowledge discoveries, it requires a careful analysis of the primary research process, namely, the original purpose, conceptualization and operationalization of the variables, and the specific context of data collection. This paper focuses on the interpretation of the secondary data as the evidence of existence or non-existence of real-world phenomena. Our discussion is based on the extended fuzzy logic approach, FLe, proposed by Lotfi Zadeh for the modeling of real-world problems. We demonstrate the necessity of an explicit model for the conceptualization and operationalization process using real-life examples from ecological and medical research.
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 | 0.172 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.020 | 0.047 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.012 |
| 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 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".