Heuristic Principles and Differential Judgments in the Assessment of Information Quality
Bibliographic record
Abstract
Information quality (IQ) is a multidimensional construct and includes dimensions such as accuracy, completeness, objectivity, and representation that are difficult to measure. Recently, research has shown that independent assessors who rated IQ yielded high inter-rater agreement for some information quality dimensions as opposed to others. In this paper, we explore the reasons that underlie the differences in the “measurability” of IQ. Employing Gigerenzer’s “building blocks” framework, we conjecture that the feasibility of using a set of heuristic principles consistently when assessing different dimensions of IQ is a key factor driving inter-rater agreement in IQ judgments. We report on two studies. In the first study, we qualitatively explored the manner in which participants applied the heuristic principles of search rules, stopping rules, and decision rules in assessing the IQ dimensions of accuracy, completeness, objectivity, and representation. In the second study, we investigated the extent to which participants could reach an agreement in rating the quality of Wikipedia articles along these dimensions. Our findings show an alignment between the consistent application of heuristic principles and inter-rater agreement levels found on particular dimensions of IQ judgments. Specifically, on the dimensions of completeness and representation, assessors applied the heuristic principles consistently and tended to agree in their ratings, whereas, on the dimensions of accuracy and objectivity, they not apply the heuristic principles in a uniform manner and inter-rater agreement was relatively low. We discuss our findings implications for research and practice.
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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 | 0.101 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".