MétaCan
Menu
Back to cohort
Record W2746522802 · doi:10.1080/87567555.2017.1348332

Social Science Boot Camp: Development and Assessment of a Foundational Course on Academic Literacy in the Social Sciences

2017· article· en· W2746522802 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCollege Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMcMaster UniversityWilfrid Laurier University
Fundersnot available
KeywordsCurriculumScientific literacyMathematics educationLiteracySocial science educationSocial studiesLearning sciencesInstitutionPedagogyScience educationSociologyPsychologySocial scienceEducational technology

Abstract

fetched live from OpenAlex

We developed a course, as part of our institution's core program, which provides students with a foundation in academic literacy in the social sciences: how to find, read, critically assess, and communicate about social science research. It is not a research methods course; rather, it is intended to introduce students to the social sciences and be better consumers of social science research. In this article, we describe the key learning objectives of this course, the basic content areas, and some of the innovative teaching and learning strategies used in the course. We also provide empirical evidence of the effectiveness of the course in meeting its learning objectives and of student responses to the course. Finally, we discuss some of the challenges in developing interdisciplinary core courses and offer suggestions for best practices for teaching social science literacy as part of the core curriculum.

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0170.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.241
GPT teacher head0.575
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it