Combining SIFT and the Information Needs, Types, and Qualities Approaches: A Framework-Informed Strategy for Information Literacy Instruction
Bibliographic record
Abstract
Increasingly, librarians are encouraged to deliver information literacy sessions that go beyond finding and using traditional scholarly sources. They are asked to cover a wider range of the information ecosystem and develop students’ capacity to critically engage with Google and the open web. In this study, we incorporated these learning objectives in an introductory computational thinking course, combining two information literacy pedagogies. For evaluating sources, we used Mike Caulfield’s SIFT technique (Stop, Investigate the source, Find better coverage; Trace claims to the original source); for resource discovery, Lane Wilkinson’s Information Needs, Types and Qualities lesson. We adapted Wilkinson’s worksheet, asked students to apply the SIFT technique to their chosen sources, and added a reflection component where students documented their research process. We conducted a content analysis of the assignment responses and coded evidence of students demonstrating knowledge practices and dispositions of two frames from the ACRL Framework, Authority is Constructed and Contextual and Searching as Strategic Exploration. Our analysis showed that students responded well to learning the SIFT technique, particularly at investigating sources; furthermore, Wilkinson’s pedagogy for source discovery encouraged students to use a wide variety of research tools. The reflection component further revealed evidence of students’ research processes and metacognition. We found this unit to be an effective means of assessing these Framework concepts, and argue that it can be applied to other topics and disciplines.
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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 | 0.013 | 0.016 |
| 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 | 0.006 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".