Two Wars, Two Narratives: A Comparative Study of AP's Coverage of 1982 and 2006 Israel-Lebanon Conflicts
Bibliographic record
Abstract
This thesis investigates how coverage of Western mainstream media, with a focus on The Associated Press news agency, changed over more than two decades by studying and comparing reporting of two Israeli wars on Lebanon: in 1982 and 2006.The two wars represent a microcosm of media coverage of the conflicts between the same two countries decades apart.Israel's effective use of public relations campaign to regain its reputation in the international mainstream media following its 1982 invasion of Lebanon and following the massacre of Palestinian refugees there, coupled with America's post-9/11 "war on terror" narrative are at the center of the evolution of media coverage of the Middle East and conflicts there, including the 2006 war.This evolution in coverage of two wars is the main theme of this thesis is explored in detail; its effects are demonstrated throughout the chapters with academic evidence as well journalistic examples almost entirely through the coverage of the AP, one of the world's largest international news organizations, whose enormous influence forms the narrative.The striking difference between AP's coverage of the two Israeli wars on Lebanon reflects the extent of the evolution of media reporting, not least because the latter war was waged in the name of counter-terrorism.The research for the thesis is foreground in extensive scholarly work, using a hybrid of academic and journalistic research.I have borrowed from the works of several academics, as well as journalists.Mainstream Western media coverage of the Middle East has evolved vastly over the more than 30 years that I have been a reporter -gradually in the first couple of decades, but more dramatically since 9/11, when Cold War ideology was all but replaced by that of "war on terror."Nowhere has this transformation been as marked as in the coverage of Middle East conflicts.In 1982, following Israel`s invasion of Lebanon, reporters could walk into Palestinian refugee camps in Beirut in the aftermath of a massacre and talk to survivors, do their own investigation, and write very much what they saw and heard.The story overwhelmingly reflected what had happened.Nowadays, when reporters come back from a story, they are not always expected to write what they saw and heard, but are often told by editors: "Here's what we need from you…" This thesis investigates how Western mainstream media coverage, with a focus on The Associated Press news agency, changed over more than two decades by studying and comparing the reporting of two Israeli wars on Lebanon: in 1982 and !
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".