Is Query Reuse Potentially Harmful? Anchoring and Adjustment in Adapting Existing Database Queries
Bibliographic record
Abstract
Reusing database queries by adapting them to satisfy new information requests is an attractive strategy for extracting information from databases without involving database specialists. However, the reuse of information systems artifacts has been shown to be susceptible to the phenomenon of anchoring and adjustment. Anchoring often leads to a systematic adjustment bias in which people fail to make sufficient changes to an anchor in response to the needs of a new task. In a study involving 157 novice query writers from six universities, we examined the effect of this phenomenon on the reuse of Structured Query Language (SQL) queries under varying levels of domain familiarity and for different types of anchors. Participants developed SQL queries to respond to four information requests in a familiar domain and four information requests in an unfamiliar domain. For two information requests in each domain, participants were also provided with sample queries (anchors) that answered similar information requests. We found evidence that the opportunity to reuse sample queries resulted in an adjustment bias leading to poorer quality query results and greater overconfidence in the correctness of results. The results also indicate that the strength of the adjustment bias depends on a combination of domain familiarity and type of anchor. This study demonstrates that anchoring and adjustment during query reuse can lead to queries that are less accurate than those written from scratch. We also extend the concept of anchoring and adjustment by distinguishing between surface-structure and deep-structure anchors and by considering the impact of domain familiarity on the adjustment bias.
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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 | 0.058 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".