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Record W4377940334 · doi:10.1007/s00216-023-04755-y

“Buddy check” peer observation activity for hands-on learning in analytical chemistry laboratories

2023· article· en· W4377940334 on OpenAlexaff
Amanda Musgrove

Bibliographic record

VenueAnalytical and Bioanalytical Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedical laboratoryAnalytical Chemistry (journal)ChemistryEngineeringEnvironmental chemistryMedicine

Abstract

fetched live from OpenAlex

Hands-on skills as taught in chemistry laboratories are well recognized as important skills to the field, both by accreditation authorities [ 1 , 2 , 3 ] and by students themselves. When asked to review their own performance in the lab, students often focus on the psychomotor skills they are learning over conceptual gains [ 4 ]. However, these skills are often neglected in favour of easier-to-assess cognitive skills when providing feedback and assessment. Hands-on skills in the lab are often evaluated in proxy, by writing about the task in a lab report or by implication based on a yield or accuracy grade rather than direct observation [ 5 ]. These types of assessments do not directly reflect the skills performance, and feedback is received well after the lab activity is complete, too late for a student to review and refine that technique.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0040.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0610.026

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 head0.049
GPT teacher head0.322
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractno

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