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Record W4410083603 · doi:10.1177/1354067x251340197

Assignment resubmission and the emergence of a third space: International students’ discursive negotiation tools

2025· article· en· W4410083603 on OpenAlexaffabout
Irene Torres-Arends

Bibliographic record

VenueCulture & Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsYorkville UniversityUniversity of Calgary
Fundersnot available
KeywordsNegotiationSpace (punctuation)SociologyPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper utilizes Gutierrez’s (2008) conceptual framework of the third space, the cultural-historical activity theory (CHAT) (Engerström, 2015), along with Bakhtin’s (1981) dialogical theory, to identify the discursive negotiation tools employed by international students at a private Canadian university when requesting the opportunity to resubmit their assignments. While this practice is not formally regulated by the institution, students frequently make these requests, supporting them with a range of arguments. During the two phases of this study, a total of 4,018 emails were collected, which are analyzed using Torres-Arends’ (2023) analytic framework to explore the reasons behind students’ requests for resubmission. The findings revealed that a third space emerged through students’ negotiation strategies in response to challenges such as meeting assignment requirements, balancing multiple responsibilities, and navigating a new academic culture. Based on these findings, two recommendations are proposed: first, that this “emergent” third space be recognized as an opportunity to redesign class materials, adjust pedagogic practices, and implement responsive course designs in culturally diverse learning environments, and second, that further research be conducted to explore professors’ perspectives on accepting or denying requests for assignment resubmission, adding a critical dimension to understanding this negotiation process.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.495
Teacher spread0.439 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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