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Record W2277047942 · doi:10.5287/ora-yj6gxpakb

Teach for America and rural southern teacher labour supply: an exploratory case study of Teach for America as a supplement to teacher labour policies in the Mississippi-Arkansas Delta, 2008-2010

2012· dissertation· en· W2277047942 on OpenAlexaboutno aff
Mallory A. Dwinal

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2012
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingGeneral partnershipEconomic shortageQuarter (Canadian coin)Economic growthWork (physics)Rural areaFace (sociological concept)GeographyPolitical scienceBusinessSociologyEconomicsEngineeringFinanceGovernment (linguistics)Archaeology

Abstract

fetched live from OpenAlex

The recent growth of Teach For America (TFA) has enabled it to substantially expand the teacher labour supply in many rural Southern communities, one of its largest and fastest-growing partnership subsets. Though it is generally accepted that these areas face more severe teacher shortages than most other regions in the country, there is little research as to how these staffing challenges arise or how they might be resolved; TFA’s potential to grow the rural Southern teacher supply thus signals a promising opportunity in need of further research. This work offers a case study of teacher labour outcomes in the Mississippi-Arkansas Delta, TFA’s oldest and largest rural Southern partnership site. In this region, local schools have experienced a 600 per-cent increase in corps member presence since 2008; consequently, TFA provided anywhere from a quarter to a half of the area’s new teacher labour supply each year from 2008 to 2010.A mixed-methods analysis illuminates both the causes of Delta teacher shortages and TFA’s potential to address these vacancies. Within the Delta, local schools face chronic teacher shortages because the communities they serve are overwhelmingly poor, geographically isolated, and racially segregated. TFA appears to have targeted the Delta communities where teacher labour policies have systematically fallen short, as it partners with districts bearing the greatest share of the region’s aggregate teacher vacancies. Additional statistical testing reveals that amongst these hard-to-staff districts, TFA has further focussed its resources into the schools that serve more rural, less educated, and/or predominantly African American populations. In this way, TFA funnels its corps members into the very districts where state reform efforts have struggled most, thus serving as a powerful resource for realigning ‘sticky’ outcomes in the most hard-to-staff Delta school districts.These findings notwithstanding, closer examination reveals significant drawbacks and limitations to current TFA outcomes in the rural Southern Delta. TFA does not saturate hard-to-staff school districts enough to produce statistically significant changes in local teacher vacancy rates. Instead, the programme appears to have established an unofficial threshold for the number of teachers placed per district; once this ceiling has been reached, additional corps members are funnelled into a new area regardless of the original district’s remaining need. Additionally, there is no long-term ‘exit strategy’ to help Delta districts employing TFA corps members to eventually cultivate their own high-quality teacher labour supply, thus leaving them perpetually dependent on TFA to staff their classrooms.Preliminary evidence suggests that state governments could address these shortcomings through 1) increased financial support for TFA to fully saturate vacancies in current partnership districts, as well as 2) the simultaneous development of grow-your-own teacher certification programmes in rural Delta districts. The evidence suggests that these two strategies would improve TFA as a targeted teacher recruitment strategy for hard-to-staff communities both in the Delta and across the programme’s nine other rural Southern partnership sites.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.309
Teacher spread0.258 · 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.

Study designQualitative
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

Citations1
Published2012
Admission routes1
Has abstractyes

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