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Record W2279665730 · doi:10.12794/metadc500080

The Impact of Target Revenue Funding on Public School Districts in North Texas

2014· dissertation· en· W2279665730 on OpenAlexaff
Dennis E. Womack

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsImpact
Fundersnot available
KeywordsRevenueGeographyPublic administrationPolitical scienceBusinessFinance

Abstract

fetched live from OpenAlex

A pre–post case study was conducted to examine how target revenue funding from Texas House Bill 1 (2006) has impacted the school districts within the Texas Education Service Center Region X area. Forced by the courts, the Texas Legislature was required to fix the Texas school finance system because of a de facto statewide property tax it had created by capping school district’s maintenance & operations tax rate at $1.50. Texas Governor Rick Perry used this opportunity to reduce school district M&O taxes by one-third. The Texas Legislature passed House Bill 1 (2006), the Public School Finance and Property Tax Relief Act, in response to the courts and to address a continuous decline in state funding support for public education. The Public School Finance and Property Tax Relief Act reduced local school districts’ property tax rates and revenue with the assurance that these funds would be exchanged for state aid. Local school property taxes were reduced over two years, 2006–2007 and 2007-2008, by 33%. In order for the State of Texas to meet the state aid funding guarantee from House Bill 1 (2006), each school district was frozen to its 2005–2006 revenue per weighted student, which was called a district’s revenue target. This study examined the impact target revenue has had on these school districts by analyzing and comparing revenues and expenditures prior to and following the law’s implementation. Specifically, changes in per-student revenue, per-student expenditures, and district fund balances were assessed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.351
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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