The Collision of Two Lexicons: Librarians, Composition Instructors and the Vocabulary of Source Evaluation
Bibliographic record
Abstract
Abstract Objective – The study has two aims. The first is to identify words and phrases from information literacy and rhetoric and composition that students used to justify the comparability of two sources. The second is to interpret the effectiveness of students’ application of these evaluative vocabularies and explore the implications for librarians and first-year composition instructors’ collaborations. Methods – A librarian and a first-year composition instructor taught a class on source evaluation using the language of information literacy, composition, and rhetorical analysis (i.e., classical, Aristotelian, rhetorical appeals). Students applied the information learned from the instruction session to help them locate and select two sources of comparable genre and rigor for the purpose of an essay assignment. The authors assessed this writing assignment for students’ evaluative diction to identify how they could improve their understanding of each other’s discourse. Results – The authors’ analysis of the student writing sample exposes struggles in how students understand, apply, and integrate the jargon of information literacy and rhetoric and composition. Assessment shows that students chose the language of rhetoric and composition rather than the language of information literacy, they selected the broadest and/or vaguest terms to evaluate their sources, and they applied circular reasoning when justifying their choices. When introduced to analogous concepts or terms between the two discourses, students cherry-picked the terms that allowed for the easiest, albeit, least-meaningful evaluations. Conclusion – The authors found that their unfamiliarity with each other’s discourse revealed itself in both the class and the student writing. They discovered that these miscommunications affected students’ language use in their written source evaluations. In fact, the authors conclude that this oversight in addressing the subtle differences between the two vocabularies was detrimental to student learning. To improve communication and students’ source evaluation, the authors consider developing a common vocabulary for more consistency between the two lexicons.
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 | 0.029 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".