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A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0009\",\n \"rows\": 22491,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| actor.id | Stable, URI-style identifier for a public Twitter account belonging to a Venezuelan opposition figure (e.g., legislators, mayors, activists, or journalists). Uniquely identifies the account across all months in the panel. |\\n| month | Month start date (YYYY-MM-01) indicating the calendar month over which tweets were aggregated. Coverage runs from 2013-01-01 through 2020-05-01. |\\n| perc_foreign_policy | Percentage of an account’s tweets in the given month that reference foreign-led policy topics (e.g., diplomacy, sanctions, international pressure, or military action). Constructed via dictionary-based text classification informed by word-embedding similarity trained on the corpus; reported as a percent (0–100), with fractional values possible (e.g., 0.78 means 0.78%). |\\n| tweeted_exile | Indicator (0/1) that the individual associated with the account was residing outside Venezuela during the month. Coded from publicly available reporting and biographical sources; months are flagged once a departure is documented and until return, where known. |\\n| log_tweets | Natural logarithm of the total number of public tweets posted by the account in the month, providing a scale-stable measure of activity volume. |\\n| actor | Text identifier for the account (often the same string as actor.id), included for readability and consistency with external sources. |\\n| months | Sequential month index starting at 1 for the first month in the dataset (January 2013) and increasing by one each calendar month. Useful for aligning time across accounts. |\\n| actorid | Numeric panel identifier for the Twitter account, facilitating joins and fixed account indexing across files or analyses. |\\n\\nDataset Context:\\nThis dataset assembles a monthly panel of Twitter activity from prominent Venezuelan opposition figures across a period of intense political and economic turbulence, spanning January 2013 to May 2020. The roster includes elected officials and well-known public voices who were active on the platform. Researchers identified accounts manually and used Twitter’s full-archive access to retrieve complete public tweet histories. Tweets were then aggregated to the account–month level to create consistent longitudinal records of online discourse.\\n\\nTo track whether individuals were inside or outside the country over time, residency status was coded from publicly available sources such as news reports, official statements, and biographical materials. Where exact dates were uncertain, the earliest documented indication of living abroad was used to anchor timing. The monthly topic measure included here—the share of tweets about foreign-led policy actions—was built using dictionaries expanded from seed terms via word-embedding similarity trained on the study corpus, capturing Venezuela-specific vocabulary and multilingual usage. Monthly tweet counts were also recorded and log-transformed to summarize posting volume.\\n\\nThe observation window covers major developments in Venezuela’s political landscape, including leadership transitions, contested elections, large protest cycles, and deepening economic and humanitarian crises. During these years, traditional media faced heavy constraints, and social media—particularly Twitter—served as a key venue for public communication, political messaging, and engagement with domestic and international audiences. Many opposition figures maintained active online presences throughout the period, and some relocated abroad; the panel structure allows their communication patterns to be followed continuously month by month. All data derive from public digital traces and public-domain biographical information, and the panel includes only months in which an account posted at least one tweet.\",\n \"columns\": {\n  \"actor.id\": \"str\",\n  \"month\": \"str\",\n  \"perc_foreign_policy\": \"float64\",\n  \"tweeted_exile\": \"int64\",\n  \"log_tweets\": \"float64\",\n  \"actor\": \"str\",\n  \"months\": \"float64\",\n  \"actorid\": \"float64\"\n }\n}\n{\n \"id\": \"ref_first\",\n \"rows\": 89,\n \"description\": \"Each actor's first month with tweeted_exile == 1 (g)\",\n \"columns\": {\n  \"actorid\": \"float64\",\n  \"g\": \"float64\"\n }\n}\n{\n \"id\": \"ref_obs\",\n \"rows\": 282,\n \"description\": \"Per actor over months 55 and 57: how many of the two months it is observed in, and its largest tweeted_exile there\",\n \"columns\": {\n  \"actorid\": \"float64\",\n  \"nboth\": \"int64\",\n  \"tmax\": \"int64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 472,\n \"description\": \"Rows of months 55 and 57: the cohort first exiled at 56 and never or not-yet (after 57) exiled actors with no exile tweet in either month, each observed in both months\",\n \"columns\": {\n  \"actorid\": \"float64\",\n  \"months\": \"float64\",\n  \"tweeted_exile\": \"int64\",\n  \"perc_foreign_policy\": \"float64\",\n  \"log_tweets\": \"float64\",\n  \"g\": \"float64\",\n  \"nboth\": \"int64\",\n  \"tmax\": \"int64\",\n  \"treated\": \"float64\",\n  \"post\": \"int64\",\n  \"treated_post\": \"float64\"\n }\n}\n\n# Question\nAmong activists who go into exile in month index 56, what is the change in their monthly share of tweets about foreign-led policy topics in the month after exile versus the month before, compared with contemporaneous activists who stayed in-country?","response":"sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"perc_foreign_policy\", \"x\": [\"treated_post\"], \"controls\": [\"treated\", \"post\", \"log_tweets\"], \"cluster\": \"actorid\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_98e88ddc","text":"sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"perc_foreign_policy","x":["treated_post"],"controls":["treated","post","log_tweets"],"cluster":"actorid"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"ec1f970220039a42d2c16e58133e5865006fc8a6f7cbd757a05cfac289a0e32d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["d26f278ad0350c0539b8b2955043e7098096a62d03e4bfcf8bcd6b1ebc52eb82"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7895408198237419}}
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