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%% SOURCE: docs/working/papers/REPLICATION_INVITE.md
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\section{Invitation to Independent Replication}
\label{sec:replication-invite}

Independent replication is invited as a scientific obligation, not a courtesy.
Complete source code, full experimental data, exact environment specifications,
and step-by-step protocols are provided. A failure to reproduce the claimed
0.00\% algorithmic coefficient of variation should be reported as a bug; it
will be investigated and acknowledged.

\subsection{Why Replicate}

Replication serves different purposes depending on the replicator's starting
position. Skeptics who believe the results are implausible can attempt to
falsify them; either a bug or an environmental difference will emerge, and both
outcomes advance understanding. Researchers who want to build on this work
should validate it first: independent replication strengthens evidence,
identifies edge cases, and establishes community trust. Researchers working on
adaptive systems gain a validated baseline for comparison and a practical
demonstration of statistical methods applied to VM tuning.

\subsection{Replication Levels}

Three tiers of replication are defined, from a 15-minute smoke test to a
multi-day full reproduction.

\subsubsection{Level 1: Smoke Test (15 minutes)}

Verifies basic functionality and the core 0\% CV claim.

\begin{lstlisting}[language=bash]
git clone https://github.com/rajames440/StarForth.git
cd StarForth
make fastest
./build/amd64/fastest/starforth --doe --config=C_FULL
\end{lstlisting}

Success criteria: build completes without errors; 780\raisebox{0.5ex}{+}
tests pass; DoE run produces cache CV $\approx 0\%$.

Failure threshold: cache CV $> 0.5\%$.

\subsubsection{Level 2: Partial Replication (4 hours)}

Reproduces the 25.4\% convergence claim across 30 trials.

\begin{lstlisting}[language=bash]
for i in {1..30}; do
  ./build/amd64/fastest/starforth --doe --config=C_FULL \
    > results/run_${i}.csv
done
python3 scripts/analyze_convergence.py results/
\end{lstlisting}

Success criteria: all 30 runs show cache CV = 0.00\%; late runs show
statistically significant improvement ($p < 0.05$); improvement magnitude
within $[20\%, 30\%]$.

Failure thresholds: cache CV $> 0.1\%$ in any run; no convergence
($p > 0.05$); improvement $< 10\%$.

\subsubsection{Level 3: Full Replication (2--3 days)}

Reproduces all five primary claims (C1--C5) across 90 trials.

\begin{lstlisting}[language=bash]
./scripts/full_replication.sh   # 3 configs x 30 runs = 90 runs
Rscript scripts/statistical_validation.R
\end{lstlisting}

Success criteria: all five claims reproduce within stated error margins;
checksums match expected values; statistical tests yield equivalent
significance levels.

\subsection{Exact Reproduction via Docker}

A Docker container provides bit-for-bit exact reproduction against a pinned
environment, eliminating environmental differences:

\begin{lstlisting}[language=bash]
docker build -t starforth-replication -f Dockerfile.replication .
docker run --rm -v $(pwd)/results:/results starforth-replication
cd results/ && sha256sum -c EXPECTED_CHECKSUMS.txt
\end{lstlisting}

If all SHA256 checksums match, the reproduction is bit-for-bit identical. If
any checksum differs and Docker is used, the discrepancy is in the code rather
than the environment.

\subsection{Manual Environment Configuration}

For manual replication outside Docker, the canonical environment is Ubuntu
22.04.3 LTS (kernel 6.2.0-39-generic), GCC 11.4.0, Make 4.3, on an x86\_64
system with AVX2 support and 16\,GB RAM. Critical configuration steps:

\begin{lstlisting}[language=bash]
# Set CPU governor
sudo cpupower frequency-set -g performance

# Disable Turbo Boost
echo 1 | sudo tee /sys/devices/system/cpu/intel_pstate/no_turbo

# Disable ASLR
echo 0 | sudo tee /proc/sys/kernel/randomize_va_space

# Pin to single core
taskset -c 0 ./build/amd64/fastest/starforth --doe
\end{lstlisting}

Expected variability for correct replication: cache CV is 0.00\% (exact
match); runtime may differ by $\pm 50\%$ (hardware-dependent); convergence
magnitude may differ by $\pm 10\%$ (CPU-specific dictionary lookup cost).

\subsection{Reporting Results}

Successful replications and failures are both scientifically valuable.
Report either via GitHub issue, using the tags \texttt{replication-success}
or \texttt{replication-failure}. Include: replicator name and institution,
date, replication level, git commit SHA, results summary (cache CV,
convergence magnitude, $p$-value), and hardware and OS specifications.

A response will follow within 48 hours: either an acknowledgment of a bug,
diagnostic questions to identify environmental differences, or a request for
additional data.

\subsection{Common Issues and Resolutions}

\paragraph{Cache CV = 0.05\%, expected 0.00\%.}
The falsification threshold is 0.1\%; a CV of 0.05\% is within acceptable
range. This constitutes a successful replication.

\paragraph{Convergence = 18\%, expected 25.4\%.}
The magnitude claim includes $\pm$ tolerance; 18\% is statistically
significant and within range for different hardware. This is a successful
replication.

\paragraph{Cache CV = 70\%, expected 0.00\%.}
This is a genuine failure. Check in order: clean rebuild (\texttt{make clean
\&\& make fastest}); ASLR disabled; no \texttt{rand()} calls in source
(\texttt{grep -r "rand(" src/}); valgrind for memory errors. Then file a
GitHub issue immediately.

\subsection{Acknowledgments for Falsification}

The first replicator who falsifies Claim C1 (determinism, CV $> 0.1\%$ under
controlled conditions) will receive co-authorship on any resulting erratum
paper. The first replicator who falsifies Claim C2 (convergence, $p > 0.05$
across 30 runs) will receive acknowledgment in future publications. Any
replicator who identifies a bug invalidating core claims will receive named
credit in the fix commit. Science advances through falsification, and
productive falsification is recognized accordingly.

\subsection{Cross-Institutional Collaboration}

Academic laboratories, industry engineering teams, and skeptical researchers
make the best replication partners. Technical support (email and video),
access to original hardware if needed, and co-authorship on any replication
study are available. Contact: \texttt{rajames440@gmail.com} (R.A.\ James).

\subsection{Replication Checklist}

Before beginning:
\begin{itemize}
  \item Read the executive summary (\S\ref{sec:executive-summary})
  \item Read the formal claim table (\S\ref{sec:formal-claims})
  \item Read the negative results (\S\ref{sec:negative-results})
  \item Choose a replication level (1, 2, or 3)
  \item Set up the environment (Docker recommended)
\end{itemize}

During replication:
\begin{itemize}
  \item Document all deviations from protocol
  \item Save all logs and outputs
  \item Record hardware and software specifications
\end{itemize}

After replication:
\begin{itemize}
  \item Compare results to expected values
  \item Calculate deviations
  \item File a report (success or failure)
\end{itemize}