Of <i>Paano-sexuals</i> and pansexuals: media representation of queer Ghanaians and queer self-representation through alternative media
Bibliographic record
Abstract
In 2021, Ghanaian Journalists formed the Coalition of Journalists against LGBT+ Rights and aligned with anti-queer campaigners to spread homophobia. This led to a raid and shutdown of the newly inaugurated LGBT+ community centre barely a month after its opening. In an interview with a media expert and human rights advocate, a journalist with a reputable media house alleged that “… pansexual means having sex with inanimate objects including a loaf of bread …” echoing well-known anti-LGBT+ campaigner, Moses Foh-Amoaning. Similarly, a renowned Ghanaian feminist, journalist, and host of a show on the national broadcaster celebrated an alleged “ex-gay” man while equating homosexuality to “a cult that is well connected.” Without a doubt, these narratives and many others prior and subsequent, have led to a series of attacks on queer people, arrests of queer people, and the introduction of the “Proper Sexual Rights and Ghanaian Family Values Bill.” In the face of this, how are queer Ghanaians and activists using alternative media to subvert mainstream/traditional media narratives? Drawing on existing literature, autoethnography, and discourse analysis, this paper explores the many ways queer Ghanaians and activists are leveraging alternative media to self-represent and counter mainstream narratives about them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".