Using School-Wide Data to Advocate for Student Success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The past three decades have seen a growing interest in improving the quality of U.S. public education, especially related to increasing the high school graduation rate. Despite the attention, the most recent national data show limited gains over the past quarter of a century. Although more than 85% of 19- to 24-year-olds nationally have earned a high school diploma or its equivalent (U.S. Department of Commerce [USDOC], 2000), approximately 5 of every 100 students over the age of 16 who were enrolled in high school in any of the past 10 years dropped out prior to completing a high school program (U.S. Department of Education [USDOE], 1999). Moreover, more than two thirds (67.3 percent) of all dropouts were 16 to 18 years old. Critically, reform efforts have done little to improve the rate at which students graduate from a regular high school program by the typical age of 18 years. And, as we discuss, wide disparities in data collection methods have made it impossible to accurately describe the academic trajectories of students and develop responsive policies based meaningful data. As a guiding metaphor for the present study, therefore, we imagined a busload of students bound for graduation. What we wanted to know was how many of the students who were expected to get the bus (i.e., those eighth graders who were presently enrolled in the school district and assigned to enroll in the high school in the fall) actually got the bus (i.e., enrolled) and then how many of these students actually got off at graduation 4 years later. Along the way, we hoped to capture an accurate picture of how many of this cohort got off prior to graduation, when and why they got off, and where they went once they got off the bus (i.e., withdrew, transferred, or were retained in grade). In addition, we were interested in identifying when and how many students got the bus along the way, either as transfers from other schools or as members of the cohort returning to the school. THE HIGH COST OF DROPPING OUT Recognizing that a 12-year curriculum is the norm for virtually every U.S. public school today, the failure to receive a high school diploma on time places millions of young Americans at risk each year. Unable to meet the minimum requirement for advanced education or entry into the workforce, dropouts experience higher unemployment rates and lower earnings than other workers (Coley, 1995; Sherraden, 1986; USDOE, 1999). Among males age 25-34, for example, the 1998 employment rate was 87% for those who received a high school diploma or were granted a General Education Diploma (GED) versus 78.5% among those who dropped out (USDOE, 2000a). Although in 1998, females were slightly more likely than males to have completed high school by age 24 (87.1% versus 84.6%; USDOE, 1999), females with a high school diploma or GED were employed far more often than those who had dropped out of school (69.5% versus 47.3%, respectively; USDOE, 1999). Significantly, failing to complete high school presents several major social consequences that include (a) forgone national income, (b) forgone tax revenues for the support of government services, (c) increased demand for social services, (d) increased crime and antisocial behavior, (e) reduced political participation, (f) reduced intergenerational mobility, and (g) poorer levels of health (Coley; Jaffe, 1998; Rumberger, 1987; Tidwell, 1988; USDOE, 1999). As the demographic face of America changes, it is instructive to view the dropout statistics by race and socioeconomic status. National statistics by race show that Hispanic students (7.8%) were more likely than Black (6.5%), White (4.0%), and Asian (5.0%) students to leave school prior to graduation (USDOE, 2000a). In addition, more young adults living in low-income families (11.0%) dropped out versus middle (5.0%) and high-income (2.1%) families (USDOE, 1999). These disparities highlight the fact that the dropout problem is most concentrated in large urban areas where poor and minority students tend to live (Coley, 1995). …
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it