Bilingual Facebook Users’ Cognitive Writing Processes / Processus cognitifs d’écriture des utilisateurs bilingues de Facebook
Bibliographic record
Abstract
This study seeks to explore the cognitive processes involved as bilinguals wrote English and Spanish Facebook status updates. Three phases of data collection were employed: individual interviews, examination of participants’ Facebook status updates and a group interview. The findings suggested that regardless of the language in which participants wrote, they made a series of decisions as they selected the content, chose the language, formulated the text and typed the status updates. The findings also indicated that for the individuals in this study, higher language proficiencies resulted in increased automaticity when converting thoughts to nonstandard online communications. While participants engaged in many experiences found in Flower and Hayes’ Cognitive Process Model (1981), additional explanation was needed to illustrate how this process varied for online bilingual writers. Cette étude cherche à explorer les processus cognitifs utilisés lorsque des personnes bilingues écrivent des statuts Facebook en anglais et en espagnol. Trois phases de collecte de données ont été utilisées : entrevues individuelles, examen des mises à jour de statut Facebook des participants et entrevue de groupe. Les résultats suggèrent que peu importe la langue de rédaction utilisée par les participants, ceux-ci procédaient à une série de décisions en sélectionnant le contenu, choisissant la langue, formulant le texte et entrant les mises à jour. Les résultats indiquent également que pour les personnes de cette étude, de meilleures compétences linguistiques se traduisaient par une automaticité accrue au moment de convertir les pensées en communications en ligne non standards. Bien que les participants aient pris part à certaines expériences décrites par Flower et Hayes dans leur Cognitive Process Model (1981), de plus amples explications sont requises pour illustrer comment ce processus varie pour les rédacteurs bilingues en ligne.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".