Poetic Representation of teacher candidate feelings on the use of Information and Communication Technologies: From interview to poem
Bibliographic record
Abstract
Given the transitional characteristics of the present moment in the country regarding the implementation of digital literacy and digital citizenship policies (Hoechsmann & DeWaard, 2015), it is important to involve as many stakeholders as possible when determining the path education will follow in the coming decades. In that sense, this study demonstrates how Poetic Representation can be employed to capture and represent a teacher candidate’s feelings regarding the use of Information and Communication Technologies in a teacher education program. It presents the procedures followed to transform a 2632-word interview transcript into a 410-word poetic format (Richardson, 2000). Such format allows for both the interpretation and representation of data to be co-created with the reader from a collaborative process of developing the poem with the participant (Sparkes, 2002). The study concludes that Poetic Representation presents a viable and valid way of integrating the voices of teacher candidates into the discussion around the use of technology in education. Moreover, the use of free, easy-to-use digital tools allowed for a comprehensive hands-on experience with the process of data collection, analysis and representation.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".