GeoLatinas: bringing down the language barrier to increase Latin American representation and democratize science communication in Earth and Planetary Sciences
Bibliographic record
Abstract
GeoLatinas is a member-driven organization that inspires, embraces and empowers Latinas to thrive in Earth and Planetary Sciences (E&PS) by creating initiatives to address and overcome career progression barriers for the representation of the Latin American community. The GeoLatinas’ Voice your needs survey, conducted in English and Spanish, showed in 2020 that many respondents in our community (42%) found language barrier as one of the most pressing issues.Perceiving English as the main communication language in the science community creates a barrier for non-native English speakers, hindering their inclusion and representation. Bilingual education in Latin American schools is uncommon. The high cost of learning and obtaining proof of English proficiency, results in limited access to higher education. The English barrier is also a challenge when publishing in indexed journals or presenting research at international events. Consequently, education and employment opportunities for aspiring scientists and professionals decrease. GeoLatinas transforms the language barrier into an opportunity by communicating in English, Spanish, and Portuguese, thus contributing to a diverse E&PS community. Specifically, we continuously develop strategies to overcome language-related issues like: (1) English as a requirement for inclusion and recognition in the science community; (2) lack of access to opportunities for Non-English speaking experts and non-experts; and (3) limited recognition of Latin American scientists’ work.Initiatives addressing the first issue include Conversando con GeoLatinas (Chatting with GeoLatinas), a space to improve English and Spanish conversational skills; Dry Runs & Peer Review, a comprehensive database of native English, Spanish and Portuguese-speaking reviewers, allowing members to receive feedback on written and oral pieces; and GeoSeminars, where leaders of GeoLatinas por Mexico host presentations in Spanish and English, with diverse experts sharing their knowledge with a broader community online. Lastly, collaborations with Nature Reviews Earth and Environment help our members publish short scientific articles in English, and Spanish or Portuguese.Regarding the second issue, the GeoLatinas Blog gives members and invited experts a platform to share their research and thoughts on diverse topics in blogs available in our three languages. In addition, GeoLatinas has fostered partnerships for specific translations to Spanish, such as Eos.org short science articles (with science communication production entity Planeteando); and also to English, like the booklet GEAS: Women who study the Earth (with the ENGIE project).Confronting the third issue, the GeoLatinas’ social-media based initiative Friday Feature in Geo has broadcasted over 160 profiles highlighting the work and contributions of Latinas in E&PS across all career stages. Together with the GeoLatinas around the world podcast in Spanish and Portuguese, we inspire and inform new generations, sharing funding opportunities and experiences from latinx scholars.As a multicultural organization, we see strength in our differences and leverage them diversifying the E&PS. Together, we nurture our multilingual communication skills and use them as high-value traits for the scientific community. By embracing our heritage and communicating science in our native languages, GeoLatinas brings down the language barrier, democratizes science communication and increases Latin American representation in science.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".