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Record W2694968048 · doi:10.1007/978-94-6091-964-0

Putting Theory into Practice

2012· book· en· W2694968048 on OpenAlexafffund

Bibliographic record

VenueSensePublishers eBooks · 2012
Typebook
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Victoria
FundersUniversité de MontréalNational Science Foundation
KeywordsProject commissioningPublishingBusinessManagementEngineeringPublic relationsPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Informal learning, also called free choice learning or out-of-school time, is a relatively new field that has grown exponentially in the past 15 years. Research on the learning and teaching that takes place in these non-traditional, non-classroom environments, such as museums, gardens, afterschool and community programs, has enjoyed tremendous growth; yet we still need to understand much more, and more deeply, how people actually interact, participate and learn in such settings. Putting Theory into Practice: Tools for Research in Informal Settings is designed as a research and practice toolkit, offering a range of theoretically well-grounded methods for assessing learning for life in diverse settings and among diverse populations. We pay special attention to the full complexity, challenges and richness involved in such research into learning in places like museums, aquariums, after-school clubs, and gardens. Putting Theory into Practice serves both, researchers and practitioners, as well as a more general audience. This book offers several field-tested methods for building empirically-based, informal learning settings and research deeply grounded and guided by theory. Sociocultural theory, broadly defined, forms the unifying theoretical framework for the different qualitative studies presented. Each chapter clearly lays out the theoretical underpinnings and how these inform the suggested methods. The chapters are written by recognized experts in the field, and each addresses, in its own way, “the synergy among different learning contexts and the benefits of studying how contexts influence learning.” Together they give voice to the diversity, richness, and complexity of the study of learners and learning for life.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.075
GPT teacher head0.373
Teacher spread0.298 · 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
GenreOther

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

Citations13
Published2012
Admission routes2
Has abstractyes

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