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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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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