Untangling search task complexity and difficulty in the context of interactive information retrieval studies
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
Purpose – One core element of interactive information retrieval (IIR) experiments is the assignment of search tasks. The purpose of this paper is to provide an analytical review of current practice in developing those search tasks to test, observe or control task complexity and difficulty. Design/methodology/approach – Over 100 prior studies of IIR were examined in terms of how each defined task complexity and/or difficulty (or related concepts) and subsequently interpreted those concepts in the development of the assigned search tasks. Findings – Search task complexity is found to include three dimensions: multiplicity of subtasks or steps, multiplicity of facets, and indeterminability. Search task difficulty is based on an interaction between the search task and the attributes of the searcher or the attributes of the search situation. The paper highlights the anomalies in our use of these two concepts, concluding with suggestions for future methodological research related to search task complexity and difficulty. Originality/value – By analyzing and synthesizing current practices, this paper provides guidance for future experiments in IIR that involve these two constructs.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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