Iqamah.Id: Media Aktivisme Kelompok Islam Inklusi Sebagai Ruang Aman Beragama
Bibliographic record
Abstract
Queer adalah salah satu isu yang masih dianggap tabu di Indonesia bahkan sering menjadi kelompok yang terdiskriminasi. Mereka membutuhkan tempat untuk saling menerima sehingga dibutuhkan media sebagai wadah aspirasi. Iqamah (Indonesian Queer Muslims and Allies) adalah komunitas yang dibentuk untuk menciptakan ruang aman beribadah. Penelitian ini bertujuan untuk mengetahui ruang aman seperti apa yang dibangun Iqamah melalui media aktivisme. Pengumpulan data dilakukan dengan cara observasi, dokumentasi dan studi pustaka. Teori yang digunakan adalah Media Aktivisme Christian Fuchs untuk menganalisis media-media apa saja yang digunakan Iqamah dalam membangun ruang aman beragama. Hasilnya Iqamah menggunakan Instagram, zoom dan zine sebagai media untuk membuat ruang aman bagi kelompok queer muslim beribadah. Kegiatan yang dilakukan yaitu Ngaji Inklusi dan Kursus seni. Selain itu, Iqamah juga melakukan gerakan sosial perubahan dengan komunitas lain di negara lain seperti Maruf Foundation, Balkan Queer Muslims, Quasa (Singapura), Desi Shia Queer Collective (India), Queer Muslim Network Toronto (Kanada), Queer Muslim Circle, Jummah for All Collective, Queer Muslim of Boston, Utah Queer Muslims, Noor Seattle dan Queer Muslim of Central Texas (Amerika Serikat).
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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.324 | 0.119 |
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".