Iowa Virtual Literacy Protocol: A Pre-Experimental Design Using Kurzweil 3000 Text-to-Speech Software with Incarcerated Adult Learners.
Bibliographic record
Abstract
The problem: The increasingly competitive global economy demands literate, educated workers.Both men and women experience the effects of education on employment rates and income.Racial and ethnic minorities, English language learners, and especially those with prison records are most deeply affected by the economic consequences of dropping out of school.The purpose of this study is to assess the effect of adaptive technology (text-to-speech software) on incarcerated low-literate adult populations.This study will determine the effectiveness of text-to-speech computer software technology with incarcerated adult learners seeking to improve literacy competencies.paying, dead-end jobs.Only 7% of dropouts 25 and older have ever made more than $40,000 a year (Johnson, 2011).In hard economic times, some will find that not having a diploma puts them at the front of the unemployment line.High school dropouts can expect to earn significantly less than high school graduates due to both income differences and employment rates.Over a lifetime, a high school dropout will earn $200,000 less than a high school graduate (Johnston, 2011).For more than 60 years, millions of adults who did not complete their formal high school studies have used the General Educational Development (GED) Tests to realize both personal satisfaction and educational and occupational opportunities.The GED Testing Program provides high-quality tests and accessible testing services for individuals who may benefit from high school diplomas or certificates, awarded by participating jurisdictions in the United States, Canada, and U.S. insular areas (American Council of Education, 2008). 1 However, obtaining a GED is no quick fix for low earnings: it takes time for substantial GED-related differences to accrue (Tyler, 2007).For example, for Black men obtaining GED certificates in prison, they do not realize immediate economic payoff until after five years (Tyler, 2007).A recent study in Florida linked GED Test information to quarterly earnings records collected by Florida's unemployment insurance system.The study included 81,170 individuals, all of whom were between the ages of 16 and 40 when they attempted the GED.Five years after achieving a GED, the GED holders' income showed a 15% gain (Tyler, Murname & Willett, 2000).But even when high school dropouts use the GED Tests to obtain basic credentials, they often decline to pursue further education, limiting their life chances in a 1 An example of an insular area is American Samoa.The Samoans have adopted their own constitution, are not American citizens, do not pay federal taxes, and control their own borders.Other examples of insular areas include Guam, U.S. Virgin Islands and Puerto Rico.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".