In the Darkness Show Me the Stars, 2021
Bibliographic record
Abstract
In the Darkness Show Me the Stars is written by Canadian multi-award winning-artists, Twin Flames. The musical explores the despair of COVID-19 victims, as well as those living with mental health and anxiety. It also captures the desperation of George Floyd’s final moments. The musical represents the alienation of ‘the people’ in a dwindling democracy, and is an awakening to the need for people to address systemic racism and sexism all over the world.\nIn the production, a crescendo builds up slowly, often under a set rhythm or music. There’s no question that 2020 was a year that built up as the days and months passed, getting louder and louder, drowning out the voices of racial minorities and making it hard for any of us to breathe. In the Darkness Show Me the Stars is a deep look into the moments of four young characters living through 2020. They are joined only by the wishes they whisper to the stars.\nWhile governments stoke the flames of racial and political division, these young ones find a way through the darkness and to one another. If you’ve ever experienced a nightmare that you’ve struggled to wake up from, only to finally yourself jolted awake, screaming, covered in sweat and unable to breathe, then you will know what each of these young characters are feeling with their own struggles.\nEach character fights to find their strength and stand up for what they believe in. They hold on to the light in the darkness and follow the guiding stars. What is revealed is a lasting change that will leave them forever connected in ways they will never fully understand. We are like constellations — imaginary boundaries formed by connecting the dots.\nBook, Music and Lyrics by Twin Flames\nDirector: Mary Francis Moore*Choreographer: Esie Mensah
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.210 | 0.076 |
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".