More than words: Text-to-speech technology as a matter of self-efficacy, self-advocacy, and choice
Bibliographic record
Abstract
This dissertation recognizes that reading is far more than decoding and identifying individual words on the printed page. Reading is not only a matter of skill and strategy, but it is also intertwined with choice, access, self-efficacy, and self-advocacy as a reader. While many students will develop these characteristics within the context of traditional print texts, there will be some who have limited access to print texts as a result of inefficient word-solving and code breaking. This ethnographic inquiry recounts the story of one classroom community nested within multiple contexts, as individual participants emerge into the world of text-to-speech technology. Students and teachers in this inquiry came to view text-to-speech technology not as an isolated support or an add-on, but as an alternative text format. It became a choice they could make similar to that of genre or author. Participants in the inquiry developed a sense of empathy for and understanding of readers who struggle and the role that technology can play in the reading process. Participant tales describe three students' discovery of text-to-speech technology and the choices they made with regard to ongoing use. This inquiry acknowledges the complexity of implementation and discusses supportive contexts for text-to-speech technology use. Results suggest that belief systems, legitimate use, time, and opportunity are pre-requisites for meaningful and authentic implementation and adoption. This inquiry adds to our understanding of how new technologies can be used to support and enhance reading in the classroom. This inquiry suggests that text-to-speech technology is a contemporary innovation that should be considered part of best practice instruction for all students, not simply those who struggle. Text-to-speech technology, offered as a whole class intervention, is one way to recognize the diversity of students and celebrate individual difference, instead of accentuating it.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".