MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexaboutno aff
Mallory A. Dwinal

Notice bibliographique

RevueOxford University Research Archive (ORA) (University of Oxford) · 2012
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueDiverse Educational Innovations Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStaffingGeneral partnershipEconomic shortageQuarter (Canadian coin)Economic growthWork (physics)Rural areaFace (sociological concept)GeographyPolitical scienceBusinessSociologyEconomicsEngineeringFinanceGovernment (linguistics)Archaeology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,099
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,309
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueOxford University Research Archive (ORA) (University of Oxford)Même sujetDiverse Educational Innovations StudiesTravaux en français237 207