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Record W4311527782 · doi:10.25071/1913-9632.39657

Remembering the Movement & Reminiscing on Achievements: An Interview with Professor Emeritus Raphael Cassimere, Jr.

2022· article· en· W4311527782 on OpenAlexvenueno aff
D. Caleb Smith

Bibliographic record

VenueLeft History An Interdisciplinary Journal of Historical Inquiry and Debate · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsChampionCivil rightsLawCivil libertiesState (computer science)Presidential systemEconomic JusticePolitical scienceDiplomacySociologyPolitics

Abstract

fetched live from OpenAlex

Raphael Cassimere, Jr. (1942-) is a nationally recognized champion of social justice and civil rights veteran. He received his B.A.(1966) and M.A. (1968) degrees in History from LSUNO (now the University of New Orleans (UNO)). In 1971, he received a PhD in History from Lehigh University in Bethlehem, Pennsylvania. He also became the first African American professor of UNO shortly after obtaining his PhD. In 2015, the institution established The Ralph Cassimere, Jr. Professorship in African American History. His rise to prominence began as an undergraduate student during the heyday of the civil rights movement. In 1960, he became president of the NAACP’s Youth Council. Since then, he has a held multiple local, regional, and national offices within the NAACP. Cassimere maintained his commitment to human and civil rights while teaching at UNO. He is a recipient of the ACLU’s Benjamin E. Smith Civil Liberties Award, the Louisiana NAACP’s Lifetime Presidential Award, U.S. State Department’s Outstanding Citizen Diplomacy Award and many of other accolades. In the following interview, Cassimere reflects on his early days in the civil rights movement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0220.010
Scholarly communication0.0070.010
Open science0.0020.005
Research integrity0.0050.020
Insufficient payload (model declined to judge)0.0040.002

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 head0.087
GPT teacher head0.359
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueLeft History An Interdisciplinary Journal of Historical Inquiry and DebateSame topicRace, History, and American SocietyFrench-language works237,207