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Spanish future of probability: teaching and learning

2014· article· en· W576119080 on OpenAlexaffvenueabout
Irina Goundareva

Bibliographic record

VenueEntrehojas Revista de Estudios Hispánicos · 2014
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGrammaticalityMeaning (existential)Computer scienceSecond-language acquisitionProcess (computing)Control (management)LinguisticsPsychologyFeature (linguistics)Mathematics educationLanguage acquisitionArtificial intelligenceGrammar

Abstract

fetched live from OpenAlex

This project investigates how the acquisition of the future of probability in Spanish can be facilitated through classroom instruction, taking into consideration what L1 English and L1 French learners of L2 Spanish bring to the process of acquisition of this linguistic feature and how language instructors may need to manipulate the input to facilitate learning. We designed an experiment which measures different outcomes of form focused and meaning based instructional methods compared to the one currently used at the University of Ottawa in the Spanish program. We have developed grammaticality judgment and limited written production tasks. We also consider long-term effects of the instruction based on the results of the delayed posttest. Our preliminary results suggest that there is an advantage of meaning based instruction over the form focused one on both tests. At the same time, both instructional methods have had more positive effect on the learner acquisition compared to the results of the control group in both explicit and implicit knowledge. As expected, French L1 learners have a slight benefit over the English L1 learners due to the positive transfer from their L1 into L2 Spanish.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 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

Citations5
Published2014
Admission routes3
Has abstractyes

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