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Record W4285277564 · doi:10.37590/able.v42.abs64

Special Place Assignments: Connecting Ecological Concepts to Each Student’s Unique LocaleThrough Scaffolded Portfolio Assignments

2022· article· en· W4285277564 on OpenAlexaff
Anne Mcintosh, Jody Rintoul

Bibliographic record

VenueAdvances in Biology Laboratory Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLocale (computer software)PortfolioEcologyComputer scienceGeographyMathematics educationBusinessBiologyMathematicsProgramming language

Abstract

fetched live from OpenAlex

In introductory biology and ecology courses and those that build on them, a central learning objective is for students to be able to describe and explain how environmental factors interact to contribute to the ecological properties (structure) and processes (function) that are observed at multiple scales. In this poster we will introduce 'special place' assignments, novel forms of assessment, that provide biology students with scaffolded opportunities to link course topics and a physical place that has unique value for them. In our introductory biology course, we have students make repeated observations of organisms in their place, which allows them the opportunity to practice formulating evolutionary and ecological questions. In our introductory ecology course students respond to a series of questions for each topic, and in our more advanced community ecology course, students reference journal articles and formulate research questions. By anchoring concepts and ideas to a special physical place, our goal is that students more meaningfully comprehend the importance and relevance of the information they learn about. This will in turn open the door to new ecological ideas and questions that will further undergraduate student learning beyond the classroom.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.390
Teacher spread0.378 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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