Bibliographic record
Abstract
Since the U.S. Border Patrol was established in 1924, agents have been an integral part of the community and have worked to educate the public on the Border Patrol mission and how they can support it. Outreach campaigns began with such programs as D.A.R.E., Red Ribbon Week, and No Mas Cruces. The campaigns were conducted via schools and traditional media such as radio, television, and print. In 2003, Border Patrol's Public Affairs Office was absorbed into the newly created Department of Homeland Security's Customs and Border Protection (CBP) agency. While Border Patrol conducts public affairs, the messaging is controlled by CBP. The prevalence of social media has provided an inexpensive, high-capacity way for Border Patrol to conduct community engagement. However, CBP retains the authority to approve social media use in an official capacity and only allows Border Patrol to use social media under the CBP umbrella. This thesis argues that Border Patrol should be allowed to use Border Patrol–specific social media accounts for community engagement and to educate the public on the Border Patrol mission. Furthermore, engagement should occur with Canadian and Mexican citizens in their native languages when possible and applicable.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".