Children’s Literate Identities: Exploring the Literacy Discourses That Permeate the Lives of Two Young Children
Bibliographic record
Abstract
This thesis reports on a multiple case study in which I explored the literate identities of two children. I examine how the literacy values, beliefs, dispositions etc. of family members, as well as those enacted during tutor sessions with me as their tutor, support or constrain children’s positive literate identities. Two children, their parents and siblings, who reside in Ontario, Canada, participated in this study. Participant observations, informal and semi-structured interviews, videos of children during tutor sessions, pictures of children’s communicative artifacts and my reflective diary, were the sources of data. Informed by Ivanic (2004) and Wohlwend (2009), I analyzed 372 interview responses for literacy discourse. I then analyzed 9853 meaning-making acts across videos for social purpose, which was informed by Peterson et al. (2019). I also coded meaning-making acts for literacy discourse. I coded 29,559 literacy discourses in total. Patterns of discourse were identified by first making comparisons across participants’ interview responses, then making comparisons between the two children’s tutor sessions. Social purpose and activity/task were also included in the comparisons between the children’s tutor sessions. I found that restrictive views of literacy, such as those pertaining to the skills-mastery discourse, were more likely to constrain children’s (re)construction of positive literate identities. I also found that participants’ interview responses predominantly reflected the skills-mastery discourse. Furthermore, children’s schools often assigned homework that enforced such restrictive views of literacy, influencing the discourses taken up during tutor sessions. I as the children’s tutor, however, played a significant role in exposing children to alternative views/discourses of literacy. My findings point to the need for tutors to move beyond a “shadow education” service model, where tutors closely follow the curricula of schools – private or public – by providing homework or test preparation support no matter the messages about literacy homework tasks enforce. This study advances research in this field by describing the connections between tutors and families’ perceptions of literacy and by highlighting how tutors can open up reflective dialogues with families, with the goal of encouraging broadened notions of literacy. My study contributes to improvements in teaching, particularly for tutors, and children’s literacy development.
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 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".