Global Communications Newsletter
Bibliographic record
Abstract
From November 30 to December 02 2022, the IEEE Latin-American Conference on Communications (LATINCOM) returned to Brazil for its fourteenth edition. LATINCOM was held in the wonderful city of Rio de Janeiro, which had the privilege to offer attendees all its fantastic beauties comprising landscapes with a series of green mountains cascading down to the coast. LATINCOM's journey to Rio de Janeiro started in Medellín, Colombia, in 2009, Bogotá, also in Colombia, in 2010. It first appeared in Brazil, Belém, in 2011. Then it moved to Cuenca, Ecuador, in 2012, Santiago, Chile, in 2013, Cartagena de Indias, Colombia, in 2014, Arequipa, Peru, in 2015, Medellin, Colombia, in 2016, Guatemala City, Guatemala, in 2017, and Guadalajara, Mexico, in 2018. LATINCOM was held for a second time in Brazil, in Salvador, 2019. The conference was forced to go online in 2019, hybrid in 2021, in Santo Domingo, Dominican Republic, and then finally returning to the face-to-face format in 2022, in Rio de Janeiro. This brought to the 14th edition a remarkable characteristic, as it represented the return to in-presence conferences after the Covid-19 outbreak. LATIN-COM is held annually and attracts submissions and participants from around the globe. In 2022, the program was organized in three intensive days, including four keynote speeches, four tutorials, two workshops, and 16 technical sessions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.011 |
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 teacher head, 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".