Bibliographic record
Abstract
In preparation for the 2020 general elections in Ghana, the leader of Ghana’s National Democratic Congress (NDC) and former President John D. Mahama picked the former vice- chancellor of the University of Cape Coast and former education minister under his administration Professor Jane Nana Opoku Agyeman as his vice-presidential candidate. This choice was met with criticism from the rank and file of the New Patriotic Party (NPP) with most of the attacks centred on her gender, rather than her meritocratic qualifications. Although Ghana has a higher population of women, they are underrepresented in parliament and political leadership. While there is sufficiently reasonable evidence supporting the assertion that women have potential that can be tapped to meaningfully enhance social, economic and political development of nations, yet very little is done in Ghana to ensure that majority of women are involved in decision-making. This chapter is a post-mortem of Ghana’s 2020 general elections, pointing to the pre-election rhetoric against the NDC’s vice presidential candidate and post-election reality. It argues that the various institutional arrangement in the numerous political parties in Ghana does not support higher female representation and the enshrined cultural perception is that the woman’s role is in the home and not politics.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".