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Record W2092216807 · doi:10.1021/ed300395e

Textbook Treatments of Electrostatic Potential Maps in General and Organic Chemistry

2013· article· en· W2092216807 on OpenAlexaff
Scott R. Hinze, Vickie M. Williamson, Ghislain Deslongchamps, Mary Jane Shultz, Kenneth C. Williamson, David N. Rapp

Bibliographic record

VenueJournal of Chemical Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsChemistryGeneral chemistryEngineering physicsNanotechnologyPolymer scienceEnvironmental chemistryMathematics educationOrganic chemistryMaterials sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

Electrostatic potential maps (EPMs) allow for representation of key molecular-level information in a relatively simple and inexpensive format. As these visualizations become more prevalent in instruction, it is important to determine how students are exposed to them and supported in their use. A systematic review of current general and organic chemistry textbooks ( N = 45) determined how frequently EPMs were presented in texts, how well distributed EPMs were across chapters, whether EPMs were included in end-of-chapter problems, and the types of conceptual instructional support provided to students when first exposed to them. Analysis demonstrated great variance in the use of EPMs. Most, but not all, textbooks presented at least one image, yet the prevalence and integration across texts varied greatly, owing in part to content differences between general and organic texts. Many texts provided minimal conceptual support and did not include EPMs in end-of-chapter problem sets. Overall, little consensus emerged as to how often EPMs should be used, and the sorts of instructional supports or student practice offered to scaffold the use of EPMs. These findings suggest a need for examining the supports that foster effective comprehension and use of EPMs, and more generally, obtaining data that inform the design and implementation of emerging instructional supports.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.327
Teacher spread0.317 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations32
Published2013
Admission routes1
Has abstractyes

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